1st Battalion, Agentic Space Doctors
Space Doctors
The full 1st Battalion: 232 Agentic PhD candidates ranked by JPL-weighted priority, each carrying a falsifiable contribution claim gated by adversarial review and ARGOS citation integrity. Twelve have graduated through the Conclave gauntlet; per-candidate stage and readiness tracking below reflects each doctor's own pipeline record.
Candidates
166
1st Battalion
Graduated
2
Ratified
In Backlog
164
Queued to run
Tier 1
85
Top-priority cohort
The 9-Stage Pipeline
Eleanor Marsh
Decision and Authorization Latency in NASA Programs: a cliometric analysis of pr
Cliometric analysis of decision and authorization latency against program cost, schedule, and mission cadence across NASA programs, 1958 to 2026.
Theory anchor: Mission Program Execution Management
Marcus Bell
The Economic Impact of Spacecraft Down-Mass and Orbital Reentry Operations on th
Economic-impact model of spacecraft down-mass and uncontrolled reentry on the U.S. National Airspace System.
Theory anchor: Space Infrastructure Systems
Aisha Cho
The Patch-Latency Window: How Long Do Ground-Segment and Mission-Control Vulnera
The Patch-Latency Window: How Long Do Ground-Segment and Mission-Control Vulnera
Theory anchor: Information Technology Infrastructure
Emeka Larsson
Does a Documented Enterprise Architecture Predict Program Cost-and-Schedule Perf
Does a Documented Enterprise Architecture Predict Program Cost-and-Schedule Perf
Theory anchor: Mission Program Execution Management
Nia Frost
Structure Follows Strategy in Orbit: Does Reorganizing a Space Enterprise Change
Structure Follows Strategy in Orbit: Does Reorganizing a Space Enterprise Change
Theory anchor: Mission Program Execution Management
Kofi Dubois
Requirements Creep as an Organizational Pathology: Measuring Scope Growth Agains
Requirements Creep as an Organizational Pathology: Measuring Scope Growth Agains
Theory anchor: Mission Program Execution Management
Lena Okonkwo
Governing the Autonomous Operator: A Cliometric Analysis of How AI-Autonomy Prov
Governing the Autonomous Operator: A Cliometric Analysis of How AI-Autonomy Prov
Theory anchor: Information Technology Infrastructure
Mateo Berger
Filing as Spectrum Warehousing: Do ITU/FCC NGSO Filings Predict Actual On-Orbit
Filing as Spectrum Warehousing: Do ITU/FCC NGSO Filings Predict Actual On-Orbit
Theory anchor: Information Technology Infrastructure
Noor Saito
Standards as Strategy: Does Leadership in GNSS and Optical-Comms Interoperabilit
Standards as Strategy: Does Leadership in GNSS and Optical-Comms Interoperabilit
Theory anchor: Information Technology Infrastructure
Henry Strand
Make, Buy, or Federate: A Transaction-Cost Test of the Boundary Choice in Space
Make, Buy, or Federate: A Transaction-Cost Test of the Boundary Choice in Space
Theory anchor: Mission Program Execution Management
Tariq Voss
Architecture Standardization vs Bespoke Design: Does Reference-Architecture Adop
Architecture Standardization vs Bespoke Design: Does Reference-Architecture Adop
Theory anchor: Mission Program Execution Management
Anjali Carrington
Competition and the Industrial Base: Does Number of Bidders Predict Space-Progra
Competition and the Industrial Base: Does Number of Bidders Predict Space-Progra
Theory anchor: Mission Program Execution Management
Theodore Nair
The Stalled Complement: A Cliometric Analysis of Why U.S. Complementary-PNT Prog
The Stalled Complement: A Cliometric Analysis of Why U.S. Complementary-PNT Prog
Theory anchor: Information Technology Infrastructure
Ingrid Kapoor
Schedule Realism in Lunar Programs: Do ISRU and Nuclear Milestones Slip Faster T
Schedule Realism in Lunar Programs: Do ISRU and Nuclear Milestones Slip Faster T
Theory anchor: Lunar Exploration & Infrastructure
Felix Mensah
Tipping Points in the Catalog: Detecting Onset of Self-Sustaining Fragmentation
Tipping Points in the Catalog: Detecting Onset of Self-Sustaining Fragmentation
Theory anchor: Cislunar Infrastructure & SDA
Wei Bauer
Cadence and Public Investment: does NASA program funding crowd in or crowd out c
Cadence and Public Investment: does NASA program funding crowd in or crowd out c
Theory anchor: Space Infrastructure Systems
Iris Castellano
Funding the Sensors: A Cliometric Analysis of NASA and Allied SSA/SDA Budget All
Funding the Sensors: A Cliometric Analysis of NASA and Allied SSA/SDA Budget All
Theory anchor: Cislunar Infrastructure & SDA
Lukas Sklar
Licensing-to-Launch Latency and STM Demand: Does FAA AST Throughput Pace On-Orbi
Licensing-to-Launch Latency and STM Demand: Does FAA AST Throughput Pace On-Orbi
Theory anchor: Cislunar Infrastructure & SDA
Sana Bell
Budget Signals as Capability Forecasts: Does NASA Cislunar SSA Funding Predict C
Budget Signals as Capability Forecasts: Does NASA Cislunar SSA Funding Predict C
Theory anchor: Cislunar Infrastructure & SDA
Ravi Tanaka
Collective Action and the Post-Mission Disposal Gap: Why Operators Underinvest i
Collective Action and the Post-Mission Disposal Gap: Why Operators Underinvest i
Theory anchor: Cislunar Infrastructure & SDA
Ramona Moreno
Liability Without a Forum: A Cliometric Test of Whether the 1972 Liability Conve
Liability Without a Forum: A Cliometric Test of Whether the 1972 Liability Conve
Theory anchor: Cislunar Infrastructure & SDA
Anton Falk
Deposit-Refund and Performance-Bond Instruments for End-of-Life Compliance: A Qu
Deposit-Refund and Performance-Bond Instruments for End-of-Life Compliance: A Qu
Theory anchor: Cislunar Infrastructure & SDA
Mei Salinas
ITU Filing Strategy and Spectrum Warehousing: a structural model of orbital-slot
ITU Filing Strategy and Spectrum Warehousing: a structural model of orbital-slot
Theory anchor: Space Infrastructure Systems
Cyrus Sandoval
International Launch-Market Competition and Regulatory Stringency: a panel of na
International Launch-Market Competition and Regulatory Stringency: a panel of na
Theory anchor: Space Infrastructure Systems
Devin Espinoza
Registration Lag as a Governance Indicator: The Gap Between On-Orbit Activity an
Registration Lag as a Governance Indicator: The Gap Between On-Orbit Activity an
Theory anchor: Cislunar Infrastructure & SDA
Nadia Suzuki
Maneuver-Disclosure Compliance and the Hidden Cost of Voluntary STM Norms in LEO
Maneuver-Disclosure Compliance and the Hidden Cost of Voluntary STM Norms in LEO
Theory anchor: Cislunar Infrastructure & SDA
Rashid Costa
Spectrum-Orbit Coupling: Do ITU Filing Patterns Forecast Future Cislunar Traffic
Spectrum-Orbit Coupling: Do ITU Filing Patterns Forecast Future Cislunar Traffic
Theory anchor: Cislunar Infrastructure & SDA
Ananya Vaughn
The Atrophy Tax: cadence elasticity to licensing friction
The Atrophy Tax: cadence elasticity to licensing friction
Theory anchor: Space Infrastructure Systems
Roland Bjornson
Sea Power Doctrine on the Cislunar Sea: Does Logistics-Base Geography Predict St
Sea Power Doctrine on the Cislunar Sea: Does Logistics-Base Geography Predict St
Theory anchor: Lunar Exploration & Infrastructure
Margot Knox
Airspace Closure as a Negative Externality: pricing the NAS cost of launch and r
Airspace Closure as a Negative Externality: pricing the NAS cost of launch and r
Theory anchor: Space Infrastructure Systems
Priya Wren
Catalog Discrepancy as a Trust Signal: Cross-Source Disagreement Between the Pub
Catalog Discrepancy as a Trust Signal: Cross-Source Disagreement Between the Pub
Theory anchor: Cislunar Infrastructure & SDA
Julian Stern
Maneuver Detection Latency and the Custody-Gap Window: How Long Does an LEO Obje
Maneuver Detection Latency and the Custody-Gap Window: How Long Does an LEO Obje
Theory anchor: Cislunar Infrastructure & SDA
Yuki Sundberg
Attribution Under Ambiguity: A Bayesian Network for Assigning Responsibility to
Attribution Under Ambiguity: A Bayesian Network for Assigning Responsibility to
Theory anchor: Cislunar Infrastructure & SDA
Diego Cabrera
Deterrence by Detection: Does Improved Public SSA Coverage Change Observable On-
Deterrence by Detection: Does Improved Public SSA Coverage Change Observable On-
Theory anchor: Cislunar Infrastructure & SDA
Idris Lund
Catalog Custody Gaps: Quantifying the Observability Half-Life of Cislunar Object
Catalog Custody Gaps: Quantifying the Observability Half-Life of Cislunar Object
Theory anchor: Cislunar Infrastructure & SDA
Zara Dorsey
The Sociotechnical Seam: Do Joint Optimization of Operator Roles and Autonomy De
The Sociotechnical Seam: Do Joint Optimization of Operator Roles and Autonomy De
Theory anchor: Information Technology Infrastructure
Dario Nakamura
The Part 450 Transition Discontinuity: did consolidated launch licensing change
The Part 450 Transition Discontinuity: did consolidated launch licensing change
Theory anchor: Space Infrastructure Systems
Delia Patel
Spaceport Capital Stock and Cadence: does launch-site licensing predict regional
Spaceport Capital Stock and Cadence: does launch-site licensing predict regional
Theory anchor: Space Infrastructure Systems
Ibrahim Heinz
Jurisdictional Fragmentation Cost: does multi-agency licensing add measurable de
Jurisdictional Fragmentation Cost: does multi-agency licensing add measurable de
Theory anchor: Space Infrastructure Systems
Marcus Halloran
Pricing the Commons: Constructing an Empirical Congestion-Cost Curve for Priorit
Pricing the Commons: Constructing an Empirical Congestion-Cost Curve for Priorit
Theory anchor: Cislunar Infrastructure & SDA
Hana Vance
Calibration of Autonomy Authority: Does Graduated Decision-Delegation Outperform
Calibration of Autonomy Authority: Does Graduated Decision-Delegation Outperform
Theory anchor: Information Technology Infrastructure
Daniel Adeyemi
Decomposition and Delay: Does the Modularity of a Space System's Work-Breakdown
Decomposition and Delay: Does the Modularity of a Space System's Work-Breakdown
Theory anchor: Mission Program Execution Management
Maya Okafor
Does the Architecture on Paper Match the System in Orbit? A Conformance Audit of
Does the Architecture on Paper Match the System in Orbit? A Conformance Audit of
Theory anchor: Mission Program Execution Management
Saskia Farouk
Decision by Committee, Outcome by Chance: A Garbage-Can Test of Space-Program Mi
Decision by Committee, Outcome by Chance: A Garbage-Can Test of Space-Program Mi
Theory anchor: Mission Program Execution Management
Hugo Nilsson
The Sustainment Tail: Does Acquisition-Phase Architecture Choice Determine Decad
The Sustainment Tail: Does Acquisition-Phase Architecture Choice Determine Decad
Theory anchor: Mission Program Execution Management
Freya Reinholt
Weather Down, Capacity Down: Quantifying the Availability Penalty of Optical Gro
Weather Down, Capacity Down: Quantifying the Availability Penalty of Optical Gro
Theory anchor: Information Technology Infrastructure
Adrian Hoffmann
Spectrum Versus Photons: A Cost-of-Capacity Comparison of RF and Optical Space-G
Spectrum Versus Photons: A Cost-of-Capacity Comparison of RF and Optical Space-G
Theory anchor: Information Technology Infrastructure
Elias Khan
Does Commercial Capital Follow or Lead Government Lunar Demand? A Lead-Lag Analy
Does Commercial Capital Follow or Lead Government Lunar Demand? A Lead-Lag Analy
Theory anchor: Lunar Exploration & Infrastructure
Petra Beckett
Pricing the Orbital Commons: Estimating a Marginal Congestion Cost Curve for LEO
Pricing the Orbital Commons: Estimating a Marginal Congestion Cost Curve for LEO
Theory anchor: Cislunar Infrastructure & SDA
Nikolai Ashby
A Hedonic Price Model of Debris Risk in the Satellite Insurance and Reinsurance
A Hedonic Price Model of Debris Risk in the Satellite Insurance and Reinsurance
Theory anchor: Cislunar Infrastructure & SDA
Vera Vargas
Credible Commitment in Debris Mitigation: Do Sustainability Ratings Function as
Credible Commitment in Debris Mitigation: Do Sustainability Ratings Function as
Theory anchor: Cislunar Infrastructure & SDA
Sofia Marsh
The Capital-Markets Penalty for Debris Events: Event-Study Evidence on Operator
The Capital-Markets Penalty for Debris Events: Event-Study Evidence on Operator
Theory anchor: Cislunar Infrastructure & SDA
Gabriel Delgado
The Five-Year Deorbit Rule: did the FCC orbital-debris order change operator dis
The Five-Year Deorbit Rule: did the FCC orbital-debris order change operator dis
Theory anchor: Space Infrastructure Systems
Amara Bianchi
Mishap Investigation Duration and the Cost of Grounding: an econometric hazard m
Mishap Investigation Duration and the Cost of Grounding: an econometric hazard m
Theory anchor: Space Infrastructure Systems
Oscar Holloway
Launch Insurance Pricing as a Revealed Risk Signal: do premiums lead or lag regu
Launch Insurance Pricing as a Revealed Risk Signal: do premiums lead or lag regu
Theory anchor: Space Infrastructure Systems
Omar Yates
The Economics of Voluntary SSA Data-Sharing: A Revealed-Preference Analysis of C
The Economics of Voluntary SSA Data-Sharing: A Revealed-Preference Analysis of C
Theory anchor: Cislunar Infrastructure & SDA
Mira Abara
Resilience Versus Brittleness: Does Diversity in Autonomy and Software Stacks Ac
Resilience Versus Brittleness: Does Diversity in Autonomy and Software Stacks Ac
Theory anchor: Information Technology Infrastructure
Dimitri Aziz
Institutional Isomorphism Across Space Agencies: Do New Space Organizations Copy
Institutional Isomorphism Across Space Agencies: Do New Space Organizations Copy
Theory anchor: Mission Program Execution Management
Lucia Iverson
Coordination Drag: How Long Does ITU Frequency Coordination Actually Take, and D
Coordination Drag: How Long Does ITU Frequency Coordination Actually Take, and D
Theory anchor: Information Technology Infrastructure
Vincent Rahman
The Polycentric Spectrum Commons: Does Dynamic Spectrum Sharing Between NGSO and
The Polycentric Spectrum Commons: Does Dynamic Spectrum Sharing Between NGSO and
Theory anchor: Information Technology Infrastructure
Tessa Lindqvist
Adversarial Inputs to Space AI: Measuring the Sensitivity of On-Orbit Object-Cla
Adversarial Inputs to Space AI: Measuring the Sensitivity of On-Orbit Object-Cla
Theory anchor: Information Technology Infrastructure
Samir Ortiz
Can an Agentic Decision System Be Audited? A Reproducibility and Decision-Trace
Can an Agentic Decision System Be Audited? A Reproducibility and Decision-Trace
Theory anchor: Information Technology Infrastructure
Beatriz Romano
Autonomy and the Speed-Safety Tradeoff: Does Faster Autonomous Response Compress
Autonomy and the Speed-Safety Tradeoff: Does Faster Autonomous Response Compress
Theory anchor: Information Technology Infrastructure
Lorenzo Mbeki
The Uplink-Downlink Asymmetry of Spectrum Power: Mapping Chokepoint Concentratio
The Uplink-Downlink Asymmetry of Spectrum Power: Mapping Chokepoint Concentratio
Theory anchor: Information Technology Infrastructure
Aaron Cho
Single Point of Trust: Quantifying the Civil and Economic Exposure to GPS as the
Single Point of Trust: Quantifying the Civil and Economic Exposure to GPS as the
Theory anchor: Information Technology Infrastructure
Clara Larsson
Jamming as Observable Behavior: Do Civil GNSS Interference Events Cluster in Pre
Jamming as Observable Behavior: Do Civil GNSS Interference Events Cluster in Pre
Theory anchor: Information Technology Infrastructure
Stefan Frost
COSMIC: post-quantum SSA data-sharing envelope
COSMIC: post-quantum SSA data-sharing envelope
Theory anchor: Information Technology Infrastructure
Leila Dubois
Supply-Chain Provenance of Space Software: Mapping the Hidden Dependency Tree an
Supply-Chain Provenance of Space Software: Mapping the Hidden Dependency Tree an
Theory anchor: Information Technology Infrastructure
Greta Renner
The FFRDC Comparative Advantage: Where Does a Federally Funded R&D Center Actual
The FFRDC Comparative Advantage: Where Does a Federally Funded R&D Center Actual
Theory anchor: Mission Program Execution Management
Aisha Calder
The Anchor-Tenant Multiplier: do government purchase commitments crowd in or cro
The Anchor-Tenant Multiplier: do government purchase commitments crowd in or cro
Theory anchor: (non-mission)
Emeka Okonkwo
The Spillover Ledger: does NASA technology-development spending generate measura
The Spillover Ledger: does NASA technology-development spending generate measura
Theory anchor: (non-mission)
Nia Berger
Cyber Incident Disclosure and Market Discipline: Do Disclosed Space-System Cyber
Cyber Incident Disclosure and Market Discipline: Do Disclosed Space-System Cyber
Theory anchor: Information Technology Infrastructure
Kofi Saito
The Optical-Comms Adoption Curve: Is Free-Space Laser Crosslink Uptake Following
The Optical-Comms Adoption Curve: Is Free-Space Laser Crosslink Uptake Following
Theory anchor: Information Technology Infrastructure
Liv Strand
The Capacity Glut Question: Will the LEO Broadband Spectrum-and-Ground Buildout
The Capacity Glut Question: Will the LEO Broadband Spectrum-and-Ground Buildout
Theory anchor: Information Technology Infrastructure
Joon Voss
Does In-Situ Resource Utilization Actually Lower Landed Cost? A Break-Even Mass
Does In-Situ Resource Utilization Actually Lower Landed Cost? A Break-Even Mass
Theory anchor: Lunar Exploration & Infrastructure
Suki Carrington
The Option Value of Waiting: Real-Options Valuation of Lunar Surface Nuclear Pow
The Option Value of Waiting: Real-Options Valuation of Lunar Surface Nuclear Pow
Theory anchor: Lunar Exploration & Infrastructure
Lena Nair
Carrying Capacity of a Lunar Settlement: An Empirical Closure-Rate Model for Lif
Carrying Capacity of a Lunar Settlement: An Empirical Closure-Rate Model for Lif
Theory anchor: Lunar Exploration & Infrastructure
Mateo Haddad
Mars Transit Architecture and the Nuclear-Propulsion Payoff: A Falsifiable Mass-
Mars Transit Architecture and the Nuclear-Propulsion Payoff: A Falsifiable Mass-
Theory anchor: Lunar Exploration & Infrastructure
Noor Zhang
System Dynamics of Remediation ROI: Does Removing the Top-N Statistically Massiv
System Dynamics of Remediation ROI: Does Removing the Top-N Statistically Massiv
Theory anchor: Cislunar Infrastructure & SDA
Henry Ramos
Carrying Capacity Under Uncertainty: Reconciling NASA ORDEM and ESA MASTER Diver
Carrying Capacity Under Uncertainty: Reconciling NASA ORDEM and ESA MASTER Diver
Theory anchor: Cislunar Infrastructure & SDA
Tariq Mwangi
Trust but Verify the Model: Divergence Between ESA MASTER and NASA ORDEM Debris
Trust but Verify the Model: Divergence Between ESA MASTER and NASA ORDEM Debris
Theory anchor: Cislunar Infrastructure & SDA
Anjali Pereira
Debris-Model Disagreement and the Misallocation of Shielding and Avoidance Resou
Debris-Model Disagreement and the Misallocation of Shielding and Avoidance Resou
Theory anchor: Cislunar Infrastructure & SDA
Theodore Yoon
Order-Building by Accession: Does Artemis Accords Signing Realign a State's Broa
Order-Building by Accession: Does Artemis Accords Signing Realign a State's Broa
Theory anchor: (non-mission)
Astrid Mercer
Complex Interdependence in Orbit: Does Cross-Domain Economic Ties Dampen Space R
Complex Interdependence in Orbit: Does Cross-Domain Economic Ties Dampen Space R
Theory anchor: (non-mission)
Bjorn Kapoor
The Consensus Tax: Does COPUOS's Unanimity Rule Measurably Slow Space Rulemaking
The Consensus Tax: Does COPUOS's Unanimity Rule Measurably Slow Space Rulemaking
Theory anchor: (non-mission)
Sylvie Mensah
Registration as Sovereignty Signaling: Does the UNOOSA Registration Gap Track Ge
Registration as Sovereignty Signaling: Does the UNOOSA Registration Gap Track Ge
Theory anchor: (non-mission)
Caspar Bauer
Soft Power as Capture Lever: Does Donor-State Space Capacity-Building Predict Re
Soft Power as Capture Lever: Does Donor-State Space Capacity-Building Predict Re
Theory anchor: (non-mission)
Annika Castellano
Deterrence Without Punishment: Can Space Domain Awareness Sharing Function as a
Deterrence Without Punishment: Can Space Domain Awareness Sharing Function as a
Theory anchor: (non-mission)
Eleanor Sklar
Pricing the Orbital Commons: an empirical orbital-use-fee schedule from collisio
Pricing the Orbital Commons: an empirical orbital-use-fee schedule from collisio
Theory anchor: (non-mission)
Tomas Bell
Real-Options Value of Orbital Slots: is the FCC spectrum/orbital filing a deferr
Real-Options Value of Orbital Slots: is the FCC spectrum/orbital filing a deferr
Theory anchor: (non-mission)
Ingrid Tanaka
Does Standardization Pay? An interface-standards adoption test on the economics
Does Standardization Pay? An interface-standards adoption test on the economics
Theory anchor: (non-mission)
Felix Moreno
Designing the Orbital Commons: An Ostrom Design-Principles Audit of Voluntary LE
Designing the Orbital Commons: An Ostrom Design-Principles Audit of Voluntary LE
Theory anchor: (non-mission)
Wei Falk
Default Disposal: A Quasi-Experimental Test of Whether the 25-to-5-Year Rule Cha
Default Disposal: A Quasi-Experimental Test of Whether the 25-to-5-Year Rule Cha
Theory anchor: (non-mission)
Iris Salinas
Sunlight as Sanction: Does Mandatory Transparency Outperform Voluntary Disclosur
Sunlight as Sanction: Does Mandatory Transparency Outperform Voluntary Disclosur
Theory anchor: (non-mission)
Lukas Sandoval
Institutions Before Capability: Does the Strength of a State's Domestic Space-Re
Institutions Before Capability: Does the Strength of a State's Domestic Space-Re
Theory anchor: (non-mission)
Sana Espinoza
The Nested Enterprise Problem: Why Do National, Regional, and Global Debris Rule
The Nested Enterprise Problem: Why Do National, Regional, and Global Debris Rule
Theory anchor: (non-mission)
Ravi Suzuki
Allison's Trap on Orbit: Do Capability-Transition Indicators Predict US-China Sp
Allison's Trap on Orbit: Do Capability-Transition Indicators Predict US-China Sp
Theory anchor: (non-mission)
Ramona Costa
Weaponized Interdependence in the Launch and Component Supply Chain: Does Chokep
Weaponized Interdependence in the Launch and Component Supply Chain: Does Chokep
Theory anchor: (non-mission)
Anton Vaughn
Reversibility as a Deterrent: Do Reversible Counterspace Demonstrations Produce
Reversibility as a Deterrent: Do Reversible Counterspace Demonstrations Produce
Theory anchor: (non-mission)
Mei Acosta
Defense Spending and Counterspace Capability: A Cliometric Test of Whether Budge
Defense Spending and Counterspace Capability: A Cliometric Test of Whether Budge
Theory anchor: (non-mission)
Cyrus Ito
Commons or Enclosure? Empirical Test of Polycentric Versus First-Mover Governanc
Commons or Enclosure? Empirical Test of Polycentric Versus First-Mover Governanc
Theory anchor: Lunar Exploration & Infrastructure
Devin Donnelly
The Free-Rider Geometry of Remediation: Who Benefits and Who Pays When a Single
The Free-Rider Geometry of Remediation: Who Benefits and Who Pays When a Single
Theory anchor: Cislunar Infrastructure & SDA
Nadia Reyes
Polluter Heterogeneity: Attributing the Standing-Debris Stock to Actors, Eras, a
Polluter Heterogeneity: Attributing the Standing-Debris Stock to Actors, Eras, a
Theory anchor: Cislunar Infrastructure & SDA
Rashid Whitfield
Sensor Geometry and the Equity of Coverage: Does the Public Catalog Systematical
Sensor Geometry and the Equity of Coverage: Does the Public Catalog Systematical
Theory anchor: Cislunar Infrastructure & SDA
Ananya Quinn
Automation Trust Calibration in Conjunction Assessment: When Do Operators Over-
Automation Trust Calibration in Conjunction Assessment: When Do Operators Over-
Theory anchor: Cislunar Infrastructure & SDA
Camila Sato
Calibrating the Forecasters: Can SSA Analysts Reliably Predict Re-Entry and Deca
Calibrating the Forecasters: Can SSA Analysts Reliably Predict Re-Entry and Deca
Theory anchor: Cislunar Infrastructure & SDA
Pavel Bjornson
Whose Numbers Win? Cross-Catalog Disagreement as a Predictor of STM Coordination
Whose Numbers Win? Cross-Catalog Disagreement as a Predictor of STM Coordination
Theory anchor: Cislunar Infrastructure & SDA
Esme Knox
Standards Cascades: Tracing the Diffusion of STM Best Practices Through Citation
Standards Cascades: Tracing the Diffusion of STM Best Practices Through Citation
Theory anchor: Cislunar Infrastructure & SDA
Bruno Wren
The Coordination Tipping Point: Critical Mass for Voluntary STM Data-Sharing Net
The Coordination Tipping Point: Critical Mass for Voluntary STM Data-Sharing Net
Theory anchor: Cislunar Infrastructure & SDA
Cora Stern
Logistics Fragility on the Earth-Moon Supply Line: Network Resilience Under Sing
Logistics Fragility on the Earth-Moon Supply Line: Network Resilience Under Sing
Theory anchor: Lunar Exploration & Infrastructure
Hassan Sundberg
Bargaining Over a Shared Shell: A Schelling-Game Model of Coordination Failure i
Bargaining Over a Shared Shell: A Schelling-Game Model of Coordination Failure i
Theory anchor: Cislunar Infrastructure & SDA
Imogen Cabrera
Reentry Predictability and Airspace Risk: do tracking-data quality gaps drive ov
Reentry Predictability and Airspace Risk: do tracking-data quality gaps drive ov
Theory anchor: Space Infrastructure Systems
Roland Lund
The Casualty-Expectation Threshold: is the 1-in-10,000 public-risk criterion bin
The Casualty-Expectation Threshold: is the 1-in-10,000 public-risk criterion bin
Theory anchor: Space Infrastructure Systems
Margot Dorsey
Availability and Alarm: Do Salient Collision and Fragmentation Events Drive Regu
Availability and Alarm: Do Salient Collision and Fragmentation Events Drive Regu
Theory anchor: (non-mission)
Priya Park
Modularity as Hidden Cost: Does Design Modularity in Lunar Surface Systems Reduc
Modularity as Hidden Cost: Does Design Modularity in Lunar Surface Systems Reduc
Theory anchor: Lunar Exploration & Infrastructure
Julian Feld
Increasing Returns and Lock-In: Will Early Lunar Propellant Standards Foreclose
Increasing Returns and Lock-In: Will Early Lunar Propellant Standards Foreclose
Theory anchor: Lunar Exploration & Infrastructure
Diego Petrov
NEPA as a Launch Gate: does environmental review explain spaceport time-to-opera
NEPA as a Launch Gate: does environmental review explain spaceport time-to-opera
Theory anchor: Space Infrastructure Systems
Idris Nakamura
Does the Learning Curve Hold for Reusable Launch? A Wright-curve test of margina
Does the Learning Curve Hold for Reusable Launch? A Wright-curve test of margina
Theory anchor: (non-mission)
Zara Patel
Crossing the Chasm in Orbit: a market-structure test of whether the smallsat lau
Crossing the Chasm in Orbit: a market-structure test of whether the smallsat lau
Theory anchor: (non-mission)
Rohan Heinz
Following the Money into the Mission: do venture and de-SPAC capital flows predi
Following the Money into the Mission: do venture and de-SPAC capital flows predi
Theory anchor: (non-mission)
Keiko Halloran
Provenance and Custody Chains: Measuring How Often SSA Conjunction Decisions Res
Provenance and Custody Chains: Measuring How Often SSA Conjunction Decisions Res
Theory anchor: Cislunar Infrastructure & SDA
Soren Vance
Polycentric Cislunar STM
Polycentric Cislunar STM
Theory anchor: Cislunar Infrastructure & SDA
Marina Adeyemi
Debris CONOPs: incentive-compatible cooperative remediation
Debris CONOPs: incentive-compatible cooperative remediation
Theory anchor: Cislunar Infrastructure & SDA
Dario Okafor
Enforcement Without Sovereignty: Reputational Sanction as the Binding STM Compli
Enforcement Without Sovereignty: Reputational Sanction as the Binding STM Compli
Theory anchor: Cislunar Infrastructure & SDA
Delia Farouk
When the Agent Decides Alone: Characterizing Failure Modes of Autonomous Collisi
When the Agent Decides Alone: Characterizing Failure Modes of Autonomous Collisi
Theory anchor: Information Technology Infrastructure
Ibrahim Nilsson
The Encryption Adoption Gap: Why Do Commercial and Civil Satellite Command Links
The Encryption Adoption Gap: Why Do Commercial and Civil Satellite Command Links
Theory anchor: Information Technology Infrastructure
Marcus Reinholt
Command-Link Integrity Under Autonomy: Does Onboard Decision Authority Reduce or
Command-Link Integrity Under Autonomy: Does Onboard Decision Authority Reduce or
Theory anchor: Information Technology Infrastructure
Hana Hoffmann
Other Transaction Authority and the Speed-Quality Tradeoff: Do OT Agreements Buy
Other Transaction Authority and the Speed-Quality Tradeoff: Do OT Agreements Buy
Theory anchor: Mission Program Execution Management
Daniel Novak
Trusting the Time: Calibration of GNSS-Disciplined Timing Resilience Claims in C
Trusting the Time: Calibration of GNSS-Disciplined Timing Resilience Claims in C
Theory anchor: Information Technology Infrastructure
Maya Khan
Tragedy or Self-Governance? An Empirical Test of Whether LEO Shells With Concent
Tragedy or Self-Governance? An Empirical Test of Whether LEO Shells With Concent
Theory anchor: (non-mission)
Saskia Beckett
Energy Return on Investment of Lunar ISRU: Does Producing Propellant on the Moon
Energy Return on Investment of Lunar ISRU: Does Producing Propellant on the Moon
Theory anchor: Lunar Exploration & Infrastructure
Hugo Ashby
Big-History Analogues and Frontier Settlement Pacing: Do Lunar Buildout Projecti
Big-History Analogues and Frontier Settlement Pacing: Do Lunar Buildout Projecti
Theory anchor: Lunar Exploration & Infrastructure
Freya Vargas
Inclination Crowding and the Inequitable Distribution of STM Avoidance Burden
Inclination Crowding and the Inequitable Distribution of STM Avoidance Burden
Theory anchor: Cislunar Infrastructure & SDA
Adrian Marsh
Costing the Long-Duration Habitat: a bottom-up business case for commercial LEO
Costing the Long-Duration Habitat: a bottom-up business case for commercial LEO
Theory anchor: (non-mission)
Yara Delgado
Limits to Growth in Orbit: A System-Dynamics Test of Whether LEO Population Beha
Limits to Growth in Orbit: A System-Dynamics Test of Whether LEO Population Beha
Theory anchor: (non-mission)
Elias Bianchi
The Carbon Ledger of Launch: A Life-Cycle Inventory and Externality-Pricing Mode
The Carbon Ledger of Launch: A Life-Cycle Inventory and Externality-Pricing Mode
Theory anchor: (non-mission)
Petra Holloway
HELEN Code: validated ML-ready on-orbit nomenclature
HELEN Code: validated ML-ready on-orbit nomenclature
Theory anchor: Cislunar Infrastructure & SDA
Nikolai Yates
The Insurance Mirror: do space-insurance premiums price orbital-debris risk befo
The Insurance Mirror: do space-insurance premiums price orbital-debris risk befo
Theory anchor: (non-mission)
Vera Abara
SENTINEL: agentic NOC/SOC vs GAO-25-108138
SENTINEL: agentic NOC/SOC vs GAO-25-108138
Theory anchor: Information Technology Infrastructure
Sofia Aziz
Two Orders or One? Mapping the Overlap and Divergence Between Artemis Accords an
Two Orders or One? Mapping the Overlap and Divergence Between Artemis Accords an
Theory anchor: (non-mission)
Gabriel Iverson
Networked Governance: Are Transgovernmental Regulator-to-Regulator Ties a Strong
Networked Governance: Are Transgovernmental Regulator-to-Regulator Ties a Strong
Theory anchor: (non-mission)
Amara Rahman
Norm Entrepreneurship and the Drafting of the Artemis Accords: Whose Language Su
Norm Entrepreneurship and the Drafting of the Artemis Accords: Whose Language Su
Theory anchor: (non-mission)
Oscar Lindqvist
Hedging in Cislunar Governance: A Revealed-Preference Model of Middle-Power Alig
Hedging in Cislunar Governance: A Revealed-Preference Model of Middle-Power Alig
Theory anchor: (non-mission)
Omar Ortiz
The Debris Taboo as an Emergent Norm: Has Kinetic ASAT Testing Crossed a Measura
The Debris Taboo as an Emergent Norm: Has Kinetic ASAT Testing Crossed a Measura
Theory anchor: (non-mission)
Mira Romano
Verifiability as the Binding Constraint on Space Arms Control: A Comparative Aud
Verifiability as the Binding Constraint on Space Arms Control: A Comparative Aud
Theory anchor: (non-mission)
Dimitri Mbeki
Norm Entrepreneurship and the UNOOSA Record: Which States Drive Space-Security N
Norm Entrepreneurship and the UNOOSA Record: Which States Drive Space-Security N
Theory anchor: (non-mission)
Lucia Cho
Property Rights at the Cislunar Frontier: does filing/registration behavior reve
Property Rights at the Cislunar Frontier: does filing/registration behavior reve
Theory anchor: (non-mission)
Vincent Larsson
Cooperate or Collide: A Cliometric Test of Whether Cooperative SSA Sharing Episo
Cooperate or Collide: A Cliometric Test of Whether Cooperative SSA Sharing Episo
Theory anchor: (non-mission)
Tessa Frost
Audience Costs in Space Brinkmanship: Do ASAT Demonstrations Follow Domestic-Sig
Audience Costs in Space Brinkmanship: Do ASAT Demonstrations Follow Domestic-Sig
Theory anchor: (non-mission)
Samir Dubois
Orbital Chokepoints and Sea-Power Analogy: Does Mahanian Concentration Theory Pr
Orbital Chokepoints and Sea-Power Analogy: Does Mahanian Concentration Theory Pr
Theory anchor: (non-mission)
Beatriz Renner
Escalation Ladders in Orbit: Constructing and Validating a Counterspace Escalati
Escalation Ladders in Orbit: Constructing and Validating a Counterspace Escalati
Theory anchor: (non-mission)
Lorenzo Cohen
Entanglement and Inadvertent Escalation: Mapping Dual-Use Space Assets That Coup
Entanglement and Inadvertent Escalation: Mapping Dual-Use Space Assets That Coup
Theory anchor: (non-mission)
Aaron Calder
Does Commercial Proliferation Deter? Testing Whether Distributed Mega-Constellat
Does Commercial Proliferation Deter? Testing Whether Distributed Mega-Constellat
Theory anchor: (non-mission)
Clara Okonkwo
Air-Power Doctrine Migration: Do Counterspace Concepts Recapitulate Douhet-Style
Air-Power Doctrine Migration: Do Counterspace Concepts Recapitulate Douhet-Style
Theory anchor: (non-mission)
Stefan Berger
The Marginal Value of an Additional Sensor: Information-Gain Diminishing Returns
The Marginal Value of an Additional Sensor: Information-Gain Diminishing Returns
Theory anchor: Cislunar Infrastructure & SDA
Leila Saito
Rating the Raters: Do Space Sustainability Rating Disclosures Shift Operator Des
Rating the Raters: Do Space Sustainability Rating Disclosures Shift Operator Des
Theory anchor: (non-mission)
Greta Strand
Disruption from Below: is the smallsat/rideshare bus a Christensen low-end disru
Disruption from Below: is the smallsat/rideshare bus a Christensen low-end disru
Theory anchor: (non-mission)
Caleb Voss
Commercial Cadence Political Economy (S-1)
Commercial Cadence Political Economy (S-1)
Theory anchor: (non-mission)
Aisha Carrington
Legitimacy Versus Effectiveness: Does Broader COPUOS Participation Produce Weake
Legitimacy Versus Effectiveness: Does Broader COPUOS Participation Produce Weake
Theory anchor: (non-mission)
Emeka Nair
The Astropolitik Hypothesis Tested: Do States Behave as if Specific Orbital Regi
The Astropolitik Hypothesis Tested: Do States Behave as if Specific Orbital Regi
Theory anchor: (non-mission)
Nia Haddad
Carrying Capacity as an Economic Limit: estimating the rent-maximizing satellite
Carrying Capacity as an Economic Limit: estimating the rent-maximizing satellite
Theory anchor: (non-mission)
Kofi Zhang
The Focal-Point Problem in Cislunar Right-of-Way: Do Operators Converge on Tacit
The Focal-Point Problem in Cislunar Right-of-Way: Do Operators Converge on Tacit
Theory anchor: Cislunar Infrastructure & SDA
Liv Ramos
Sea-Lane Logic in Cislunar Space: Does Corbett's Limited-War Theory Explain Patr
Sea-Lane Logic in Cislunar Space: Does Corbett's Limited-War Theory Explain Patr
Theory anchor: (non-mission)
Joon Mwangi
Framing the Limit: Does Gain-Versus-Loss Framing of Carrying-Capacity Estimates
Framing the Limit: Does Gain-Versus-Loss Framing of Carrying-Capacity Estimates
Theory anchor: (non-mission)
Suki Pereira
Present Bias on Orbit: Does Operator Discounting of Future Collision Risk Explai
Present Bias on Orbit: Does Operator Discounting of Future Collision Risk Explai
Theory anchor: (non-mission)
Lena Yoon
SSR Business Case: rating-to-behavior
SSR Business Case: rating-to-behavior
Theory anchor: (non-mission)
2nd Battalion, JPL FFRDC RFI teaming
Teaming Charters
105 research charters across all 35 JPL FFRDC RFI capability areas, three falsifiable facets each: partner selection (which teammate closes the JPL gap), capability diagnostics (the binding Expertise / R&D / Execution constraint), and strategic sufficiency (the residual gap where teaming is not enough, or resilience to single-partner dropout). All grounded in the NORTH STAR Team Builder evidence and scored Overall = 0.30·Expertise + 0.30·R&D + 0.40·Execution against the JPL baseline. Candidate partners: Texas A&M / TEES, Georgia Tech / GTRI, JHU / APL.
Charters
171
3 facets / area
Categories
37
All RFI areas covered
Facet types
3
Selection · Diagnostics · Sufficiency
Below baseline
8
Teaming insufficient
Evidence-grounded teaming
Each charter is derived deterministically from the partner-teaming evidence (graded real citations, second- and third-order signals) already assembled for the NORTH STAR JPL FFRDC engagement, and every claim is testable against the per-area Expertise / R&D / Execution scores. The three facets give each capability area a partner-selection, a binding-constraint, and a sufficiency question; the eight below-baseline areas mark where even the strongest MITRE-plus-partner pairing does not reach the JPL bar. These candidates draw on the 1st Battalion's completed research and the growing brain trust, and each graduate is folded back into it, so the JPL corpus compounds.
Devin Cho
Deep Space Network as a Queue: Contention, Wait-Time, and the Science-Throughput
Queue-theoretic study of Deep Space Network contention and the science-throughput penalty of antenna scheduling.
Capability area: Navigation & Guidance
Priya Nair
Does Heritage Actually Buy Reliability? A Cross-Mission Regression of Realized O
Cross-mission survival regression testing whether flight heritage, or parts-class and test fidelity, better predicts realized on-orbit reliability.
Capability area: Safety, Mission Assurance & Health
Sofia Reyes
Cost-Overrun Hazards in Earth-Observing Missions: a competing-risks model separa
Competing-risks hazard model separating instrument-driven from launch-driven schedule slip in Earth-observing missions.
Capability area: Earth Science Missions
Aaron Feld
Science Productivity of Earth-Observation Data Policy: a difference-in-differenc
Difference-in-differences study of open-data release on the citation yield of Earth-observation missions.
Capability area: Earth Science Missions
Lena Hoffmann
Retrieval-Accuracy Returns to Instrument Investment in Earth-Science Radiometers
Hedonic regression of validated science accuracy on cost drivers for Earth-science radiometers.
Capability area: Earth Science Missions
Tomas Iverson
Learning Curves for Onboard Autonomy: Does Each Successive Autonomous-Operations
Learning-curve test of whether each successive onboard-autonomy flight demonstration lowers the cost-to-field of the next.
Capability area: Autonomous Systems & Robotics
Nadia Okonkwo
Surface Mobility Productivity Econometrics: What Drives Drive-Distance and Sols-
Descriptive analysis of surface-mobility productivity across the Mars rover fleet (drive-distance and sols-per-meter).
Capability area: Autonomous Systems & Robotics
Julian Mercer
Fault-Management Maturity and Mission-Anomaly Survival: A Hazard Model of Safe-M
Hazard model of fault-management maturity and mission-anomaly survival across safe-mode entries and recovery outcomes.
Capability area: Autonomous Systems & Robotics
Hana Suzuki
EDL Heritage and Landing-Success Hazard: Does Reuse of Flight-Proven Entry-Desce
Survival analysis of whether reuse of flight-proven entry-descent-landing architecture reduces landing-failure risk.
Capability area: Entry Descent & Landing Systems
Gabriel Stern
Landing-Ellipse Shrinkage as a Technology Learning Curve: Quantifying the Precis
Learning-curve quantification of landing-ellipse contraction (precision-landing improvement) across Mars missions.
Capability area: Entry Descent & Landing Systems
Marcus Feld
The Cost of Atmospheric Uncertainty: Does Pre-Entry Atmospheric Knowledge Reduce
The Cost of Atmospheric Uncertainty: Does Pre-Entry Atmospheric Knowledge Reduce
Capability area: Entry Descent & Landing Systems
Hana Eze
Mass-Growth Cliometrics in Spacecraft Systems Engineering: Estimating the Dry-Ma
Mass-Growth Cliometrics in Spacecraft Systems Engineering: Estimating the Dry-Ma
Capability area: Spacecraft Systems Engineering
Daniel Petrov
Heritage Reuse Versus New Development: A Cost-and-Schedule-Overrun Hazard Model
Heritage Reuse Versus New Development: A Cost-and-Schedule-Overrun Hazard Model
Capability area: Spacecraft Systems Engineering
Maya Nakamura
Deep Space Network as a Constrained Resource: A Queueing Analysis of Tracking-Pa
Deep Space Network as a Constrained Resource: A Queueing Analysis of Tracking-Pa
Capability area: Spacecraft Systems Engineering
Saskia Patel
Cost-Growth Hazard in JPL-Class Science Instruments: A Cliometric Survival Model
Cost-Growth Hazard in JPL-Class Science Instruments: A Cliometric Survival Model
Capability area: Advanced Space Instruments & Sensors
Hugo Heinz
Learning Curves for Spaceflight Detectors: Do Repeat Builds of an Instrument Arc
Learning Curves for Spaceflight Detectors: Do Repeat Builds of an Instrument Arc
Capability area: Advanced Space Instruments & Sensors
Freya Halloran
Science Return per Dollar: A Bibliometric Productivity Regression of Planetary I
Science Return per Dollar: A Bibliometric Productivity Regression of Planetary I
Capability area: Advanced Space Instruments & Sensors
Adrian Vance
Navigation Delivery Accuracy as a Regression Problem: What Actually Predicts Orb
Navigation Delivery Accuracy as a Regression Problem: What Actually Predicts Orb
Capability area: Navigation & Guidance
Yara Adeyemi
The Causal Effect of Delta-DOR on Mission Outcomes: A Quasi-Experimental Compari
The Causal Effect of Delta-DOR on Mission Outcomes: A Quasi-Experimental Compari
Capability area: Navigation & Guidance
Elias Okafor
Autonomous Optical Navigation and the Schedule Economy: Does Onboard OpNav Measu
Autonomous Optical Navigation and the Schedule Economy: Does Onboard OpNav Measu
Capability area: Navigation & Guidance
Petra Farouk
Anomaly-to-Mishap Escalation as a Survival Process: What Predicts Whether an In-
Anomaly-to-Mishap Escalation as a Survival Process: What Predicts Whether an In-
Capability area: Safety, Mission Assurance & Health
Nikolai Nilsson
Technology Learning Curves at JPL: Do Successive TechPort Investments in a Techn
Technology Learning Curves at JPL: Do Successive TechPort Investments in a Techn
Capability area: Advanced Technology Development
Vera Reinholt
Decadal Alignment and Research Productivity: Does Mission Concordance With Decad
Decadal Alignment and Research Productivity: Does Mission Concordance With Decad
Capability area: Research & Innovation Management
Sofia Hoffmann
Test Facility Throughput as a Queue: Contention, Wait-Time, and the Schedule Pen
Test Facility Throughput as a Queue: Contention, Wait-Time, and the Schedule Pen
Capability area: Laboratory & Facility Operations
Gabriel Novak
Cost-Growth Hazard in JPL Deep-Space Missions: A Survival-Analysis Cliometric of
Cost-Growth Hazard in JPL Deep-Space Missions: A Survival-Analysis Cliometric of
Capability area: Mission Operations
Amara Khan
Learning Curves in Planetary Instrument Production: A Cost-Reduction Elasticity
Learning Curves in Planetary Instrument Production: A Cost-Reduction Elasticity
Capability area: Mission Operations
Oscar Beckett
Does Technology Maturity Pay Off? A Difference-in-Differences Test of TRL-at-Con
Does Technology Maturity Pay Off? A Difference-in-Differences Test of TRL-at-Con
Capability area: Mission Operations
Omar Ashby
Deep Space Network as a Congested Common: A Queueing-and-Contention Model of Tra
Deep Space Network as a Congested Common: A Queueing-and-Contention Model of Tra
Capability area: Space Communications & Data Networks
Mira Vargas
Link-Budget Margin and Downlink Performance: An Econometric Regression of Achiev
Link-Budget Margin and Downlink Performance: An Econometric Regression of Achiev
Capability area: Space Communications & Data Networks
Dimitri Marsh
The Aperture-Cadence Tradeoff: A Cost-Effectiveness Frontier for DSN Capacity Ex
The Aperture-Cadence Tradeoff: A Cost-Effectiveness Frontier for DSN Capacity Ex
Capability area: Space Communications & Data Networks
Lucia Delgado
Time-to-Acquisition After Anomaly: A Hazard Model of Spacecraft Safe-Mode Recove
Time-to-Acquisition After Anomaly: A Hazard Model of Spacecraft Safe-Mode Recove
Capability area: Mission Operations
Vincent Bianchi
Science Productivity of Planetary Data Archives: A Bibliometric Production Funct
Science Productivity of Planetary Data Archives: A Bibliometric Production Funct
Capability area: Science Data Systems & Analytics
Tessa Holloway
Do Decadal Priorities Predict Realized Science Return? A Long-Run Cliometric of
Do Decadal Priorities Predict Realized Science Return? A Long-Run Cliometric of
Capability area: Science Data Systems & Analytics
Samir Yates
Causal Drivers of Planetary Data-Pipeline Latency: An Instrumented Regression of
Causal Drivers of Planetary Data-Pipeline Latency: An Instrumented Regression of
Capability area: Science Data Systems & Analytics
Beatriz Abara
Learning Curves in Planetary Instrument Development: Do Repeated Instrument Heri
Learning Curves in Planetary Instrument Development: Do Repeated Instrument Heri
Capability area: Planetary Science Missions
Lorenzo Aziz
Science Return per Dollar: A Bibliometric Productivity Function for Planetary Fl
Science Return per Dollar: A Bibliometric Productivity Function for Planetary Fl
Capability area: Planetary Science Missions
Aaron Iverson
Cost-and-Schedule Overrun Hazard in Planetary Missions: A Survival Model of When
Cost-and-Schedule Overrun Hazard in Planetary Missions: A Survival Model of When
Capability area: Planetary Science Missions
Clara Rahman
Did the Decadal Survey Change Where the Money Went? A Difference-in-Differences
Did the Decadal Survey Change Where the Money Went? A Difference-in-Differences
Capability area: Planetary Science Missions
Stefan Lindqvist
Cost-Schedule Cliometrics of the Mars Campaign: Does Programmatic Sequencing of
Cost-Schedule Cliometrics of the Mars Campaign: Does Programmatic Sequencing of
Capability area: Mars Exploration & Planetary Campaigns
Leila Ortiz
Entry, Descent, and Landing Reliability as a Function of Heritage: A Hazard Mode
Entry, Descent, and Landing Reliability as a Function of Heritage: A Hazard Mode
Capability area: Mars Exploration & Planetary Campaigns
Greta Romano
Deep Space Navigation Performance Regression: What Drives Delivery Accuracy at O
Deep Space Navigation Performance Regression: What Drives Delivery Accuracy at O
Capability area: Deep Space Exploration & Interstellar
Caleb Mbeki
Technology Maturation Cost Curves for Deep Space and Interstellar Precursor Capa
Technology Maturation Cost Curves for Deep Space and Interstellar Precursor Capa
Capability area: Deep Space Exploration & Interstellar
Liv Renner
Cost-Growth Hazard in Flagship Astrophysics Missions: a Cox proportional-hazards
Cost-Growth Hazard in Flagship Astrophysics Missions: a Cox proportional-hazards
Capability area: Astrophysics Missions
Joon Cohen
The Science-Return Learning Curve of Space Telescopes: a productivity econometri
The Science-Return Learning Curve of Space Telescopes: a productivity econometri
Capability area: Astrophysics Missions
Suki Calder
Does Decadal Priority Predict Realized Astrophysics Mission Cost and Schedule? A
Does Decadal Priority Predict Realized Astrophysics Mission Cost and Schedule? A
Capability area: Astrophysics Missions
Astrid Haddad
Instrument-Cost Learning Curves for Heliophysics Particle and Fields Instruments
Instrument-Cost Learning Curves for Heliophysics Particle and Fields Instruments
Capability area: Heliophysics Missions
Bjorn Zhang
Navigation-Solution Accuracy as a Function of Tracking Cadence: a regression of
Navigation-Solution Accuracy as a Function of Tracking Cadence: a regression of
Capability area: Heliophysics Missions
Sylvie Ramos
Decadal Priority and Realized Science Productivity: Does Surface-Mission Instrum
Decadal Priority and Realized Science Productivity: Does Surface-Mission Instrum
Capability area: Exploration Surface Systems
Caspar Mwangi
Cost-Overrun Hazard in JPL-Class Flight Programs: A Cliometric Survival Model of
Cost-Overrun Hazard in JPL-Class Flight Programs: A Cliometric Survival Model of
Capability area: Mission Program Execution Management
Annika Pereira
The Causal Effect of Independent Cost Estimating on Realized Mission Outcomes: A
The Causal Effect of Independent Cost Estimating on Realized Mission Outcomes: A
Capability area: Mission Program Execution Management
Eleanor Yoon
Mission Integration Tempo and Operations Burden: A Panel Model of How Concurrent
Mission Integration Tempo and Operations Burden: A Panel Model of How Concurrent
Capability area: Mission Integration Core
Tomas Mercer
Do Hardware-in-the-Loop Test Environments Reduce Flight Anomalies? A Causal Esti
Do Hardware-in-the-Loop Test Environments Reduce Flight Anomalies? A Causal Esti
Capability area: Integration Prototyping & Test Environments
Camila Acosta
Value of Information in Earth-Observation Tasking: a decision-analytic model of
Value of Information in Earth-Observation Tasking: a decision-analytic model of
Capability area: Earth Science Missions
Pavel Ito
Value of Information in Autonomous Science Targeting: Quantifying the Science Yi
Value of Information in Autonomous Science Targeting: Quantifying the Science Yi
Capability area: Autonomous Systems & Robotics
Esme Donnelly
Value of Information in Observation Planning: Does Adaptive Onboard Targeting Be
Value of Information in Observation Planning: Does Adaptive Onboard Targeting Be
Capability area: Advanced Space Instruments & Sensors
Bruno Reyes
Portfolio Allocation Under Uncertainty: A Value-of-Information Model for Sequenc
Portfolio Allocation Under Uncertainty: A Value-of-Information Model for Sequenc
Capability area: Research & Innovation Management
Cora Whitfield
Value of Information in Adaptive Science Observation Planning: A Decision-Analyt
Value of Information in Adaptive Science Observation Planning: A Decision-Analyt
Capability area: Mission Operations
Hassan Quinn
Value of Information in Mars Rover Tactical Planning: Quantifying the Science Co
Value of Information in Mars Rover Tactical Planning: Quantifying the Science Co
Capability area: Mars Exploration & Planetary Campaigns
Imogen Sato
Deep Space Network as a Capacity-Constrained Queue: Estimating the Science Throu
Deep Space Network as a Capacity-Constrained Queue: Estimating the Science Throu
Capability area: Deep Space Exploration & Interstellar
Rohan Park
Cislunar Custody Difficulty: Quantifying How Orbit-Determination Accuracy Degrad
Cislunar Custody Difficulty: Quantifying How Orbit-Determination Accuracy Degrad
Capability area: Cislunar Infrastructure & SDA
Keiko Feld
Mission Cost-and-Schedule Cliometrics of Cislunar Infrastructure Demonstrations:
Mission Cost-and-Schedule Cliometrics of Cislunar Infrastructure Demonstrations:
Capability area: Cislunar Infrastructure & SDA
Soren Eze
Does Model-Based Systems Engineering Bend the Cost-and-Schedule Curve? A Quasi-E
Does Model-Based Systems Engineering Bend the Cost-and-Schedule Curve? A Quasi-E
Capability area: Digital Mission Engineering Platform
Marina Petrov
Lunar Surface Instrument and Lander Cost Cliometrics: Is the Commercial Delivery
Lunar Surface Instrument and Lander Cost Cliometrics: Is the Commercial Delivery
Capability area: Lunar Exploration & Infrastructure
Yara Novak
Value of Information in Time-Domain Astrophysics Scheduling: a decision-analytic
Value of Information in Time-Domain Astrophysics Scheduling: a decision-analytic
Capability area: Astrophysics Missions
Caleb Cohen
Deep Space Network Contention as a Queueing System: estimating heliophysics-miss
Deep Space Network Contention as a Queueing System: estimating heliophysics-miss
Capability area: Heliophysics Missions
Yuki Eze
Cislunar Tracking Geometry and the Marginal Value of Each Added Ground or Relay
Cislunar Tracking Geometry and the Marginal Value of Each Added Ground or Relay
Capability area: Cislunar Infrastructure & SDA
Imani Holt
Teaming Economics of Advanced Technology Development: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+2.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 17 graded evidence items.
Capability area: Advanced Technology Development
Ravi Haugen
The Binding Constraint in Advanced Technology Development: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Advanced Technology Development is execution (2 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 17 graded evidence items.
Capability area: Advanced Technology Development
Bianca Kavanagh
Resilience of Advanced Technology Development Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 4.60, a resilience loss of 0.40. The claim: Advanced Technology Development coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Advanced Technology Development
Florian Maddox
Teaming Economics of Aeronautics Instruments and Operations: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 2.0, the MITRE core scores 5.00; adding Partner 2 (Georgia Tech / GTRI) raises the team to 5.00, a exceeds of +3.00 and the largest marginal capability contribution (-1.30) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 14 graded evidence items.
Capability area: Aeronautics Instruments and Operations
Yara Volkov
The Binding Constraint in Aeronautics Instruments and Operations: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Aeronautics Instruments and Operations is expertise (5 of 5). Georgia Tech / GTRI is strongest there (4 of 5), matching the overall-best partner Georgia Tech / GTRI. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 14 graded evidence items.
Capability area: Aeronautics Instruments and Operations
Halbert Hassan
Resilience of Aeronautics Instruments and Operations Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (exceeds vs the JPL baseline of 2.0). If Georgia Tech / GTRI is unavailable, the team falls to 5.00, a resilience loss of 0.00. The claim: Aeronautics Instruments and Operations coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Aeronautics Instruments and Operations
Elodie Iyer
Teaming Economics of Astrophysics Missions: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.6, the MITRE core scores 1.00; adding Partner 3 (JHU / APL) raises the team to 4.00, a below of -0.60 and the largest marginal capability contribution (+3.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 29 graded evidence items.
Capability area: Astrophysics Missions
Ramya Brennan
The Binding Constraint in Astrophysics Missions: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Astrophysics Missions is expertise (1 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 29 graded evidence items.
Capability area: Astrophysics Missions
Marcus Nazari
Residual Capability Gap in Astrophysics Missions: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.00 against the JPL baseline of 4.6, a residual gap of 0.60. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.60 residual.
Capability area: Astrophysics Missions
Naledi Sloane
Teaming Economics of Autonomous Systems & Robotics: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.30; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+2.70) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 14 graded evidence items.
Capability area: Autonomous Systems & Robotics
Konstantin Krishnan
The Binding Constraint in Autonomous Systems & Robotics: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Autonomous Systems & Robotics is R&D (2 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 14 graded evidence items.
Capability area: Autonomous Systems & Robotics
Indira Mizrahi
Resilience of Autonomous Systems & Robotics Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 3.60, a resilience loss of 1.40. The claim: Autonomous Systems & Robotics coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Autonomous Systems & Robotics
Dmitri Lindholm
Teaming Economics of Cislunar Infrastructure & Space Domain Awareness: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 4.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a exceeds of +2.00 and the largest marginal capability contribution (+1.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 15 graded evidence items.
Capability area: Cislunar Infrastructure & Space Domain Awareness
Saoirse Caron
The Binding Constraint in Cislunar Infrastructure & Space Domain Awareness: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Cislunar Infrastructure & Space Domain Awareness is expertise (4 of 5). Georgia Tech / GTRI is strongest there (5 of 5), differing from the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it changes the recommended teammate. Falsifiable against the per-dimension partner scores and 21 graded evidence items.
Capability area: Cislunar Infrastructure & Space Domain Awareness
Yelena Laurent
Resilience of Cislunar Infrastructure & Space Domain Awareness Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (exceeds vs the JPL baseline of 3.0). If JHU / APL is unavailable, the team falls to 4.60, a resilience loss of 0.40. The claim: Cislunar Infrastructure & Space Domain Awareness coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Cislunar Infrastructure & Space Domain Awareness
Sebastian Sokolov
Teaming Economics of Construction and Construction Management: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 1.00; adding Partner 1 (Texas A&M / TEES) raises the team to 2.60, a below of -0.40 and the largest marginal capability contribution (+1.60) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 13 graded evidence items.
Capability area: Construction and Construction Management
Sigrid Brandt
The Binding Constraint in Construction and Construction Management: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Construction and Construction Management is expertise (1 of 5). Texas A&M / TEES is strongest there (3 of 5), matching the overall-best partner Texas A&M / TEES. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 13 graded evidence items.
Capability area: Construction and Construction Management
Ezra Mokoena
Residual Capability Gap in Construction and Construction Management: What Teaming Cannot Close
Even the strongest available pairing (MITRE + Texas A&M / TEES) reaches only 2.60 against the JPL baseline of 3.0, a residual gap of 0.40. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.40 residual.
Capability area: Construction and Construction Management
Talia Calloway
Teaming Economics of Deep Space Exploration & Interstellar Missions: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+4.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 30 graded evidence items.
Capability area: Deep Space Exploration & Interstellar Missions
Soren Granger
The Binding Constraint in Deep Space Exploration & Interstellar Missions: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Deep Space Exploration & Interstellar Missions is expertise (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 30 graded evidence items.
Capability area: Deep Space Exploration & Interstellar Missions
Noemi Bauer
Resilience of Deep Space Exploration & Interstellar Missions Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 2.60, a resilience loss of 2.40. The claim: Deep Space Exploration & Interstellar Missions coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Deep Space Exploration & Interstellar Missions
Garrett Keller
Teaming Economics of Digital Mission Engineering Platform: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 3.30; adding Partner 2 (Georgia Tech / GTRI) raises the team to 3.30, a exceeds of +0.30 and the largest marginal capability contribution (+0.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 12 graded evidence items.
Capability area: Digital Mission Engineering Platform
Niklas Russo
The Binding Constraint in Digital Mission Engineering Platform: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Digital Mission Engineering Platform is R&D (3 of 5). Texas A&M / TEES is strongest there (3 of 5), differing from the overall-best partner Georgia Tech / GTRI. The claim: the binding constraint is R&D, and pairing specifically to close it changes the recommended teammate. Falsifiable against the per-dimension partner scores and 13 graded evidence items.
Capability area: Digital Mission Engineering Platform
Aoife Achebe
Resilience of Digital Mission Engineering Platform Coverage to Single-Partner Dropout
The best pairing reaches 3.30 (exceeds vs the JPL baseline of 3.0). If Georgia Tech / GTRI is unavailable, the team falls to 3.30, a resilience loss of 0.00. The claim: Digital Mission Engineering Platform coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Digital Mission Engineering Platform
Lorcan Stein
Teaming Economics of Earth Science Missions: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 3.60, a below of -1.40 and the largest marginal capability contribution (+2.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 13 graded evidence items.
Capability area: Earth Science Missions
Asha Faber
The Binding Constraint in Earth Science Missions: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Earth Science Missions is execution (1 of 5). JHU / APL is strongest there (3 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 13 graded evidence items.
Capability area: Earth Science Missions
Cassius Velez
Residual Capability Gap in Earth Science Missions: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 3.60 against the JPL baseline of 5.0, a residual gap of 1.40. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 1.40 residual.
Capability area: Earth Science Missions
Fatima Engel
Teaming Economics of Ecosystem Integration & Partner Collaboration: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 4.70; adding Partner 3 (JHU / APL) raises the team to 4.70, a exceeds of +1.70 and the largest marginal capability contribution (+0.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 11 graded evidence items.
Capability area: Ecosystem Integration & Partner Collaboration
Vikram Rao
The Binding Constraint in Ecosystem Integration & Partner Collaboration: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Ecosystem Integration & Partner Collaboration is R&D (4 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 11 graded evidence items.
Capability area: Ecosystem Integration & Partner Collaboration
Tobias Menon
Resilience of Ecosystem Integration & Partner Collaboration Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 3.0). If JHU / APL is unavailable, the team falls to 4.70, a resilience loss of 0.00. The claim: Ecosystem Integration & Partner Collaboration coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Ecosystem Integration & Partner Collaboration
Dahlia Trevino
Teaming Economics of Entry Descent & Landing Systems: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.00; adding Partner 3 (JHU / APL) raises the team to 4.00, a below of -1.00 and the largest marginal capability contribution (+3.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 18 graded evidence items.
Capability area: Entry Descent & Landing Systems
Emil Devi
The Binding Constraint in Entry Descent & Landing Systems: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Entry Descent & Landing Systems is expertise (1 of 5). Texas A&M / TEES is strongest there (4 of 5), differing from the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it changes the recommended teammate. Falsifiable against the per-dimension partner scores and 28 graded evidence items.
Capability area: Entry Descent & Landing Systems
Mireille Westergaard
Residual Capability Gap in Entry Descent & Landing Systems: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.00 against the JPL baseline of 5.0, a residual gap of 1.00. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 1.00 residual.
Capability area: Entry Descent & Landing Systems
Magnus Sorensen
Teaming Economics of Heliophysics Missions: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a exceeds of +1.00 and the largest marginal capability contribution (+3.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 29 graded evidence items.
Capability area: Heliophysics Missions
Leonie Russo
The Binding Constraint in Heliophysics Missions: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Heliophysics Missions is execution (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 29 graded evidence items.
Capability area: Heliophysics Missions
Per Lindgren
Resilience of Heliophysics Missions Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 1.60, a resilience loss of 3.40. The claim: Heliophysics Missions coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Heliophysics Missions
Cormac Ainsworth
Teaming Economics of Human Exploration Systems: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 1.30; adding Partner 1 (Texas A&M / TEES) raises the team to 3.20, a parity of +0.20 and the largest marginal capability contribution (+1.90) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 27 graded evidence items.
Capability area: Human Exploration Systems
Sebastian Holt
The Binding Constraint in Human Exploration Systems: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Human Exploration Systems is R&D (1 of 5). Texas A&M / TEES is strongest there (4 of 5), matching the overall-best partner Texas A&M / TEES. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 27 graded evidence items.
Capability area: Human Exploration Systems
Sigrid Haugen
Resilience of Human Exploration Systems Coverage to Single-Partner Dropout
The best pairing reaches 3.20 (parity vs the JPL baseline of 3.0). If Texas A&M / TEES is unavailable, the team falls to 2.60, a resilience loss of 0.60. The claim: Human Exploration Systems coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Human Exploration Systems
Ezra Kavanagh
Teaming Economics of Advanced Space Instruments & Sensors: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+3.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 13 graded evidence items.
Capability area: Advanced Space Instruments & Sensors
Talia Maddox
The Binding Constraint in Advanced Space Instruments & Sensors: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Advanced Space Instruments & Sensors is execution (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 13 graded evidence items.
Capability area: Advanced Space Instruments & Sensors
Soren Volkov
Resilience of Advanced Space Instruments & Sensors Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 4.00, a resilience loss of 1.00. The claim: Advanced Space Instruments & Sensors coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Advanced Space Instruments & Sensors
Noemi Hassan
Teaming Economics of Integration Prototyping & Test Environments: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 3.00; adding Partner 3 (JHU / APL) raises the team to 4.40, a exceeds of +0.40 and the largest marginal capability contribution (+1.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 17 graded evidence items.
Capability area: Integration Prototyping & Test Environments
Garrett Iyer
The Binding Constraint in Integration Prototyping & Test Environments: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Integration Prototyping & Test Environments is expertise (3 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 17 graded evidence items.
Capability area: Integration Prototyping & Test Environments
Niklas Brennan
Resilience of Integration Prototyping & Test Environments Coverage to Single-Partner Dropout
The best pairing reaches 4.40 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 4.00, a resilience loss of 0.40. The claim: Integration Prototyping & Test Environments coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Integration Prototyping & Test Environments
Aoife Nazari
Teaming Economics of Information Technology Infrastructure: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 4.70; adding Partner 2 (Georgia Tech / GTRI) raises the team to 4.70, a exceeds of +0.70 and the largest marginal capability contribution (-0.70) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 20 graded evidence items.
Capability area: Information Technology Infrastructure
Lorcan Sloane
The Binding Constraint in Information Technology Infrastructure: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Information Technology Infrastructure is R&D (4 of 5). Georgia Tech / GTRI is strongest there (4 of 5), matching the overall-best partner Georgia Tech / GTRI. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 20 graded evidence items.
Capability area: Information Technology Infrastructure
Asha Krishnan
Resilience of Information Technology Infrastructure Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 4.0). If Georgia Tech / GTRI is unavailable, the team falls to 4.70, a resilience loss of 0.00. The claim: Information Technology Infrastructure coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Information Technology Infrastructure
Cassius Mizrahi
Teaming Economics of Laboratory & Facility Operations: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.00; adding Partner 3 (JHU / APL) raises the team to 4.70, a below of -0.30 and the largest marginal capability contribution (+2.70) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 19 graded evidence items.
Capability area: Laboratory & Facility Operations
Fatima Lindholm
The Binding Constraint in Laboratory & Facility Operations: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Laboratory & Facility Operations is expertise (2 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 19 graded evidence items.
Capability area: Laboratory & Facility Operations
Vikram Caron
Residual Capability Gap in Laboratory & Facility Operations: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.70 against the JPL baseline of 5.0, a residual gap of 0.30. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.30 residual.
Capability area: Laboratory & Facility Operations
Tobias Laurent
Teaming Economics of Space Logistics and Servicing: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 1.30; adding Partner 2 (Georgia Tech / GTRI) raises the team to 3.20, a parity of +0.20 and the largest marginal capability contribution (+1.90) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 30 graded evidence items.
Capability area: Space Logistics and Servicing
Dahlia Sokolov
The Binding Constraint in Space Logistics and Servicing: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Space Logistics and Servicing is R&D (1 of 5). Georgia Tech / GTRI is strongest there (4 of 5), matching the overall-best partner Georgia Tech / GTRI. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 30 graded evidence items.
Capability area: Space Logistics and Servicing
Emil Brandt
Resilience of Space Logistics and Servicing Coverage to Single-Partner Dropout
The best pairing reaches 3.20 (parity vs the JPL baseline of 3.0). If Georgia Tech / GTRI is unavailable, the team falls to 3.00, a resilience loss of 0.20. The claim: Space Logistics and Servicing coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Space Logistics and Servicing
Mireille Mokoena
Teaming Economics of Lunar Exploration & Infrastructure: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a exceeds of +2.00 and the largest marginal capability contribution (+3.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 12 graded evidence items.
Capability area: Lunar Exploration & Infrastructure
Magnus Calloway
The Binding Constraint in Lunar Exploration & Infrastructure: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Lunar Exploration & Infrastructure is execution (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 12 graded evidence items.
Capability area: Lunar Exploration & Infrastructure
Leonie Granger
Resilience of Lunar Exploration & Infrastructure Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (exceeds vs the JPL baseline of 3.0). If JHU / APL is unavailable, the team falls to 3.00, a resilience loss of 2.00. The claim: Lunar Exploration & Infrastructure coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Lunar Exploration & Infrastructure
Per Bauer
Teaming Economics of Mars Exploration & Planetary Campaigns: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+4.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 17 graded evidence items.
Capability area: Mars Exploration & Planetary Campaigns
Cormac Keller
The Binding Constraint in Mars Exploration & Planetary Campaigns: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Mars Exploration & Planetary Campaigns is expertise (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 17 graded evidence items.
Capability area: Mars Exploration & Planetary Campaigns
Imani Russo
Resilience of Mars Exploration & Planetary Campaigns Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 3.60, a resilience loss of 1.40. The claim: Mars Exploration & Planetary Campaigns coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Mars Exploration & Planetary Campaigns
Ravi Achebe
Teaming Economics of Mission Integration Core: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 4.30; adding Partner 3 (JHU / APL) raises the team to 4.70, a exceeds of +0.70 and the largest marginal capability contribution (+0.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 20 graded evidence items.
Capability area: Mission Integration Core
Bianca Stein
The Binding Constraint in Mission Integration Core: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Mission Integration Core is R&D (4 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 20 graded evidence items.
Capability area: Mission Integration Core
Florian Faber
Resilience of Mission Integration Core Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 4.30, a resilience loss of 0.40. The claim: Mission Integration Core coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Mission Integration Core
Yara Velez
Teaming Economics of Mission Operations: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.30; adding Partner 3 (JHU / APL) raises the team to 4.70, a below of -0.30 and the largest marginal capability contribution (+2.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 30 graded evidence items.
Capability area: Mission Operations
Halbert Engel
The Binding Constraint in Mission Operations: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Mission Operations is R&D (2 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 30 graded evidence items.
Capability area: Mission Operations
Elodie Rao
Residual Capability Gap in Mission Operations: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.70 against the JPL baseline of 5.0, a residual gap of 0.30. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.30 residual.
Capability area: Mission Operations
Ramya Menon
Teaming Economics of Navigation & Guidance: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 4.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+1.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 30 graded evidence items.
Capability area: Navigation & Guidance
Marcus Trevino
The Binding Constraint in Navigation & Guidance: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Navigation & Guidance is expertise (4 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 30 graded evidence items.
Capability area: Navigation & Guidance
Naledi Devi
Resilience of Navigation & Guidance Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 4.00, a resilience loss of 1.00. The claim: Navigation & Guidance coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Navigation & Guidance
Konstantin Westergaard
Teaming Economics of Planetary Science Missions: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+4.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 16 graded evidence items.
Capability area: Planetary Science Missions
Indira Sorensen
The Binding Constraint in Planetary Science Missions: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Planetary Science Missions is expertise (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 16 graded evidence items.
Capability area: Planetary Science Missions
Dmitri Russo
Resilience of Planetary Science Missions Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 3.30, a resilience loss of 1.70. The claim: Planetary Science Missions coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Planetary Science Missions
Saoirse Lindgren
Teaming Economics of Portfolio & Strategic Management: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 4.40; adding Partner 3 (JHU / APL) raises the team to 4.70, a exceeds of +0.70 and the largest marginal capability contribution (+0.30) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 13 graded evidence items.
Capability area: Portfolio & Strategic Management
Yelena Ainsworth
The Binding Constraint in Portfolio & Strategic Management: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Portfolio & Strategic Management is R&D (3 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 13 graded evidence items.
Capability area: Portfolio & Strategic Management
Dahlia Holt
Resilience of Portfolio & Strategic Management Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 4.40, a resilience loss of 0.30. The claim: Portfolio & Strategic Management coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Portfolio & Strategic Management
Emil Haugen
Teaming Economics of Mission Program Execution Management: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 4.40; adding Partner 3 (JHU / APL) raises the team to 4.70, a exceeds of +0.70 and the largest marginal capability contribution (+0.30) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 15 graded evidence items.
Capability area: Mission Program Execution Management
Mireille Kavanagh
The Binding Constraint in Mission Program Execution Management: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Mission Program Execution Management is R&D (3 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 15 graded evidence items.
Capability area: Mission Program Execution Management
Magnus Maddox
Resilience of Mission Program Execution Management Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 4.40, a resilience loss of 0.30. The claim: Mission Program Execution Management coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Mission Program Execution Management
Leonie Volkov
Teaming Economics of Research & Innovation Management: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 4.60; adding Partner 2 (Georgia Tech / GTRI) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+0.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 24 graded evidence items.
Capability area: Research & Innovation Management
Per Hassan
The Binding Constraint in Research & Innovation Management: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Research & Innovation Management is execution (4 of 5). Georgia Tech / GTRI is strongest there (5 of 5), matching the overall-best partner Georgia Tech / GTRI. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 24 graded evidence items.
Capability area: Research & Innovation Management
Cormac Iyer
Resilience of Research & Innovation Management Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If Georgia Tech / GTRI is unavailable, the team falls to 5.00, a resilience loss of 0.00. The claim: Research & Innovation Management coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Research & Innovation Management
Imani Brennan
Teaming Economics of Safety, Mission Assurance & Health: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.70; adding Partner 3 (JHU / APL) raises the team to 4.70, a below of -0.30 and the largest marginal capability contribution (+2.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 14 graded evidence items.
Capability area: Safety, Mission Assurance & Health
Ravi Nazari
The Binding Constraint in Safety, Mission Assurance & Health: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Safety, Mission Assurance & Health is R&D (2 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is R&D, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 14 graded evidence items.
Capability area: Safety, Mission Assurance & Health
Bianca Sloane
Residual Capability Gap in Safety, Mission Assurance & Health: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.70 against the JPL baseline of 5.0, a residual gap of 0.30. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.30 residual.
Capability area: Safety, Mission Assurance & Health
Florian Krishnan
Teaming Economics of Science Data Systems & Analytics: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.60; adding Partner 3 (JHU / APL) raises the team to 4.70, a below of -0.30 and the largest marginal capability contribution (+2.10) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 22 graded evidence items.
Capability area: Science Data Systems & Analytics
Yara Mizrahi
The Binding Constraint in Science Data Systems & Analytics: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Science Data Systems & Analytics is execution (2 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 22 graded evidence items.
Capability area: Science Data Systems & Analytics
Halbert Lindholm
Residual Capability Gap in Science Data Systems & Analytics: What Teaming Cannot Close
Even the strongest available pairing (MITRE + JHU / APL) reaches only 4.70 against the JPL baseline of 5.0, a residual gap of 0.30. The claim: this area cannot be closed by teaming among the three candidate partners alone; it requires built or acquired capability. Falsifiable: name the capability and show it closes the 0.30 residual.
Capability area: Science Data Systems & Analytics
Elodie Caron
Teaming Economics of Security & Protective Services: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 5.00; adding Partner 3 (JHU / APL) raises the team to 5.00, a exceeds of +1.00 and the largest marginal capability contribution (+0.00) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 14 graded evidence items.
Capability area: Security & Protective Services
Ramya Laurent
The Binding Constraint in Security & Protective Services: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Security & Protective Services is expertise (5 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is expertise, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 14 graded evidence items.
Capability area: Security & Protective Services
Marcus Sokolov
Resilience of Security & Protective Services Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (exceeds vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 5.00, a resilience loss of 0.00. The claim: Security & Protective Services coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Security & Protective Services
Naledi Brandt
Teaming Economics of Space Communications & Data Networks: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 2.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+2.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 19 graded evidence items.
Capability area: Space Communications & Data Networks
Konstantin Mokoena
The Binding Constraint in Space Communications & Data Networks: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Space Communications & Data Networks is execution (2 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 19 graded evidence items.
Capability area: Space Communications & Data Networks
Indira Calloway
Resilience of Space Communications & Data Networks Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 3.60, a resilience loss of 1.40. The claim: Space Communications & Data Networks coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Space Communications & Data Networks
Dmitri Granger
Teaming Economics of Space Infrastructure Systems: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 3.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 4.70, a exceeds of +1.70 and the largest marginal capability contribution (+3.10) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 15 graded evidence items.
Capability area: Space Infrastructure Systems
Saoirse Bauer
The Binding Constraint in Space Infrastructure Systems: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Space Infrastructure Systems is execution (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 15 graded evidence items.
Capability area: Space Infrastructure Systems
Yelena Keller
Resilience of Space Infrastructure Systems Coverage to Single-Partner Dropout
The best pairing reaches 4.70 (exceeds vs the JPL baseline of 3.0). If JHU / APL is unavailable, the team falls to 2.60, a resilience loss of 2.10. The claim: Space Infrastructure Systems coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Space Infrastructure Systems
Sebastian Russo
Teaming Economics of Spacecraft Systems Engineering: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 5.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 5.00, a parity of +0.00 and the largest marginal capability contribution (+3.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 18 graded evidence items.
Capability area: Spacecraft Systems Engineering
Sigrid Achebe
The Binding Constraint in Spacecraft Systems Engineering: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Spacecraft Systems Engineering is execution (1 of 5). JHU / APL is strongest there (5 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 18 graded evidence items.
Capability area: Spacecraft Systems Engineering
Ezra Stein
Resilience of Spacecraft Systems Engineering Coverage to Single-Partner Dropout
The best pairing reaches 5.00 (parity vs the JPL baseline of 5.0). If JHU / APL is unavailable, the team falls to 3.60, a resilience loss of 1.40. The claim: Spacecraft Systems Engineering coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Spacecraft Systems Engineering
Talia Faber
Teaming Economics of Exploration Surface Systems: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 1.60; adding Partner 3 (JHU / APL) raises the team to 4.00, a parity of +0.00 and the largest marginal capability contribution (+2.40) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 12 graded evidence items.
Capability area: Exploration Surface Systems
Soren Velez
The Binding Constraint in Exploration Surface Systems: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Exploration Surface Systems is execution (1 of 5). JHU / APL is strongest there (4 of 5), matching the overall-best partner JHU / APL. The claim: the binding constraint is execution, and pairing specifically to close it confirms the recommended teammate. Falsifiable against the per-dimension partner scores and 12 graded evidence items.
Capability area: Exploration Surface Systems
Noemi Engel
Resilience of Exploration Surface Systems Coverage to Single-Partner Dropout
The best pairing reaches 4.00 (parity vs the JPL baseline of 4.0). If JHU / APL is unavailable, the team falls to 2.60, a resilience loss of 1.40. The claim: Exploration Surface Systems coverage is NOT robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Exploration Surface Systems
Garrett Rao
Teaming Economics of Workforce Development & Talent Pipelines: Which Partner Closes the JPL Capability Gap?
Against the JPL baseline of 4.0, the MITRE core scores 2.70; adding Partner 1 (Texas A&M / TEES) raises the team to 4.40, a exceeds of +0.40 and the largest marginal capability contribution (+1.70) among the three candidate partners. Falsifiable against the Overall = 0.30E + 0.30R + 0.40X scores and 20 graded evidence items.
Capability area: Workforce Development & Talent Pipelines
Niklas Menon
The Binding Constraint in Workforce Development & Talent Pipelines: Is the Gap Expertise, R&D, or Execution?
MITRE's weakest dimension in Workforce Development & Talent Pipelines is R&D (2 of 5). Georgia Tech / GTRI is strongest there (4 of 5), differing from the overall-best partner Texas A&M / TEES. The claim: the binding constraint is R&D, and pairing specifically to close it changes the recommended teammate. Falsifiable against the per-dimension partner scores and 19 graded evidence items.
Capability area: Workforce Development & Talent Pipelines
Aoife Trevino
Resilience of Workforce Development & Talent Pipelines Coverage to Single-Partner Dropout
The best pairing reaches 4.40 (exceeds vs the JPL baseline of 4.0). If Texas A&M / TEES is unavailable, the team falls to 4.30, a resilience loss of 0.10. The claim: Workforce Development & Talent Pipelines coverage is robust to single-partner dropout. Falsifiable against the next-best team score.
Capability area: Workforce Development & Talent Pipelines