AI, LLMs, autonomy & digital twins

Packet identity

Packet ID

systems:ai-autonomy

Packet SHA-256 identity

533f9d7d5a5c0633ed9f3dfecd1bc1d0437d1bbf523ec9bf80b50c896d930237

Corpus SHA-256 identity

8fa944604ca189f5a9216ca59f640ad2ca20972ad512f2f4970764716782e18d

Release ID

public-alpha-2026-07-26-research-visuals-r14

Source commit

3eb036fce3d711336c8c605625895e8a2e799ab0

Frozen corpus date

2026-07-25

Primary records

24

Reference sources

35

Questions and exclusions

Required questions

1. Required question 1 (exact ID: question-1)
   Does each evidence basis, readiness level, confidence level, rationale, and locator match the frozen sources?
2. Required question 2 (exact ID: question-2)
   Are dependencies, failure modes, precursors, unknowns, Earthside benefits, and the stop gate technically and ethically bounded?
3. Required question 3 (exact ID: question-3)
   Which conclusion should be approved, revised, contested, rejected, or recused from at its current fingerprint?

Explicit exclusions

• A system packet is not a complete spacecraft design, feasibility proof, safety case, or launch authorization.
• Context sources do not become direct support unless the record says so with an exact locator and relation.

Requested controlled scopes: ai-knowledge-assurance, governance-law-rights, information-science, systems-engineering

Frozen-evidence decision window: 365 days from the packet freeze. Not applicable to this packet family.

Complete primary record set

Every record below has one primary packet owner. Decisions must bind to the exact record and packet fingerprints; a changed lesson body, evidence grade, citation, locator, source snapshot, requirement, policy, release, or commit expires the old packet.

1. system · ai-autonomy

   AI, LLMs, autonomy & digital twins

   Record fingerprint

   28806d124fc870b3b3a457af967c5acdf047bcb5cca01431b50801a388f3e43d

   Minimum approvals

   1

   Required scope groups

   bounded-competence: ai-knowledge-assurance, governance-law-rights

   High-consequence domains

   None under the named two-person rule

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   Slug

   ai-autonomy

   Name

   AI, LLMs, autonomy & digital twins

   Short Name

   AI & autonomy

   Index

   11

   Thesis

   AI may change specialist workloads and the speed of learning, but every safety-critical function must remain safe and operable when every generative model is unavailable, compromised, stale, or intentionally isolated.

   Current State

   1. JPL’s Deep Space 1 Remote Agent flight experiment demonstrated bounded onboard planning, execution, and response to simulated faults; it did not demonstrate indefinite autonomous operation.
   2. NASA’s Starling demonstrations address distributed multi-spacecraft autonomy as a separate mission and evidence class.
   3. LLMs may serve bounded advisory uses such as retrieval, tutoring, translation, incident summarization, or candidate plans only with approved source revisions, exact locators, conflict handling, abstention, and independent authority. A signature can establish integrity and authenticity, not truth, currency, authorization, or safety.
   4. Digital-twin methods support specific testing and operational tasks within declared validity envelopes; every model is partial and may diverge as physical systems, configurations, and environments change.

   Unknowns

   1. This foundation corpus contains no published demonstration of a generative or autonomous system maintaining safe, legitimate judgment for a diverse crew across generations.
   2. Confabulation, invented citations, prompt or tool injection, poisoned procedures or telemetry, stale signed material, automation bias, privacy leakage, evaluator contamination, sensor spoofing, and correlated model/runtime failure threaten epistemic resilience.
   3. Robots still lack the broad manipulation, diagnosis, fabrication, and self-repair needed to maintain a worldship.

   Earth Benefits

   1. Bounded offline operations support, digital-twin fault ranges, knowledge arks, source provenance, skill retention, and AI-off drills may benefit remote industry, disaster response, and long-lived institutions when their limits are independently evaluated.

   Dependencies

   1. cybersecurity
   2. manufacturing-isru
   3. institutions-workforce

   Gate

   Require independently assessed safety properties, separate physical protection layers, explicit authority gates, tested workload and timing bounds, audited provenance, bounded abstention, manual operation, recovery drills, and independent appeal.

   Sources

   1. Title

      JPL Remote Agent

      URL

      https://www.jpl.nasa.gov/nmp/ds1/tech/autora.html↗ (opens external site in a new tab)

      Kind

      Primary or institutional source

   2. Title

      NASA Distributed Spacecraft Autonomy

      URL

      https://www.nasa.gov/centers-and-facilities/ames/what-is-nasas-distributed-spacecraft-autonomy/↗ (opens external site in a new tab)

      Kind

      Primary or institutional source

   3. Title

      NIST Generative AI Profile

      URL

      https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence↗ (opens external site in a new tab)

      Kind

      Primary or institutional source

   4. Title

      NIST Adversarial Machine Learning Taxonomy

      URL

      https://csrc.nist.gov/pubs/ai/100/2/e2025/final↗ (opens external site in a new tab)

      Kind

      Primary or institutional source

   Failure Modes

   1. Models drift, hallucinate, or inherit poisoned data while appearing authoritative to a crew that cannot call outside experts.
   2. A common model, sensor, power source, or update process defeats supposedly independent autonomous systems.

   Precursors

   1. Offline, provenance-preserving assistants for maintenance, science, and remote infrastructure with auditable citations.
   2. Formal verification, adversarial evaluation, graceful degradation, and human-recovery drills on bounded safety functions.

2. claim · claim-11-01

   AI may change specialist workloads and the speed of learning, but every safety-critical function must remain safe and operable when every generative model is unavailable, compromised, stale, or intentionally isolated.

   Record fingerprint

   2b2c6728e9308b88365e344f701b7c321ba36223ed5b702c44f79b6011f567f1

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, human-factors, life-support-continuity

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-01

   System Slug

   ai-autonomy

   Kind

   thesis

   Statement Ref

   Field

   thesis

   Statement

   AI may change specialist workloads and the speed of learning, but every safety-critical function must remain safe and operable when every generative model is unavailable, compromised, stale, or intentionally isolated.

   Statement Fingerprint

   5632818743c650b44a28b0b19bc91a93aeb6dc6bd1c2b828cb185d8103e44d83

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   normative

   Readiness

   Early research (exact value: early-research)

   Confidence

   supported

   Rationale

   Current AI risk and generative-AI guidance identifies useful human-AI configurations alongside confabulation, information-integrity, privacy, security, and overreliance risks. NASA software assurance establishes an evidence discipline for safety-critical software. Requiring safe AI-off operation is a GShips resilience boundary; the cited sources do not demonstrate it across a closed habitat or generations.

   Citations

   1. Source ID

      src-ak-nist-ai-rmf-100-1

      Locator

      Sections 3 and 4 on AI risks, trustworthy characteristics, human-AI interaction, and the Govern, Map, Measure, and Manage functions.

      Relation

      Direct normative authority (exact value: direct-normative-authority)

   2. Source ID

      src-pa-nist-ai-600-1

      Locator

      Confabulation, human-AI configuration, information integrity, data privacy, value-chain risk, measurement, incident, and disclosure sections.

      Relation

      Direct normative authority (exact value: direct-normative-authority)

   3. Source ID

      src-ak-nasa-software-assurance-87398b

      Locator

      Sections 1 through 4 and requirements mapping on lifecycle software assurance, software safety, objective evidence, security, independence, IV&V, maintenance, and retirement.

      Relation

      Direct method (exact value: direct-method)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   Representative long-duration trials showing that safety-critical functions remain equally or more reliable when they depend on a generative model, including model loss, compromise, staleness, runtime failure, and succession of operators, could narrow this boundary. Evidence of unsafe AI-off workload would require redesign rather than silent dependence.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. life-support-continuity
   4. human-factors

3. claim · claim-11-02

   JPL’s Deep Space 1 Remote Agent flight experiment demonstrated bounded onboard planning, execution, and response to simulated faults; it did not demonstrate indefinite autonomous operation.

   Record fingerprint

   38271e4711b5d6e7f7c6d2dfeb4c92a067b527bf2ccb4a97448e71da4384b806

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, spacecraft-safety

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-02

   System Slug

   ai-autonomy

   Kind

   current state

   Statement Ref

   Field

   currentState

   Index

   0

   Statement

   JPL’s Deep Space 1 Remote Agent flight experiment demonstrated bounded onboard planning, execution, and response to simulated faults; it did not demonstrate indefinite autonomous operation.

   Statement Fingerprint

   38139ce5ca5d4acf488ddbf1aff25fb661ab4a717e1022f6ad30098d8e3eecb3

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   demonstrated

   Readiness

   operational

   Confidence

   strong

   Rationale

   JPL's official Deep Space 1 record documents onboard planning and execution of selected subsystem activities, four injected simulated faults, a timing bug, a paused experiment, and later completion with ground-team involvement. That evidence supports a bounded flight demonstration and explicitly does not establish indefinite autonomous operation.

   Citations

   1. Source ID

      src-ak-nasa-ds1-remote-agent

      Locator

      Remote Agent experiment description covering high-level goals, onboard planning and execution, selected subsystems, four simulated faults, the timing error, experiment pause, ground diagnosis, and resumed run.

      Relation

      Direct demonstration (exact value: direct-demonstration)

   2. Source ID

      src-mp-nasa-se-handbook

      Locator

      Lifecycle, technology maturation, verification, validation, configuration management, technical risk, and the need to retain the tested system and operating-context boundary.

      Relation

      Scope boundary (exact value: scope-boundary)

   3. Source ID

      src-ak-nasa-software-assurance-87398b

      Locator

      Lifecycle objective-evidence, software-safety, security, IV&V, anomaly, maintenance, and retirement requirements relevant to interpreting a bounded software flight test.

      Relation

      Context only (exact value: context-only)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   A corrected primary mission record showing that Remote Agent did not perform the described onboard functions would change the demonstrated assessment. Longer deployments with independently reported operating duration, fault coverage, hardware scope, ground support, maintenance, and unresolved anomalies would change the readiness boundary, not the historical result.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. spacecraft-safety

4. claim · claim-11-03

   NASA’s Starling demonstrations address distributed multi-spacecraft autonomy as a separate mission and evidence class.

   Record fingerprint

   77f0e6c3c5d9c49ce7e4af054dbcf2fe76ad2da3bd702417d4593e5eff14272a

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, spacecraft-safety

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-03

   System Slug

   ai-autonomy

   Kind

   current state

   Statement Ref

   Field

   currentState

   Index

   1

   Statement

   NASA’s Starling demonstrations address distributed multi-spacecraft autonomy as a separate mission and evidence class.

   Statement Fingerprint

   c54c1011b881a8bdc691d715e5bd9e5562f64ea0532a32fdb4db9fd644828aff

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   demonstrated

   Readiness

   Early research (exact value: early-research)

   Confidence

   strong

   Rationale

   NASA's flight-results record describes Starling as a four-CubeSat technology demonstration and reports distributed science autonomy across three spacecraft, crosslink networking, navigation, and maneuver-planning outcomes with explicit limitations. Those objectives, architecture, date, and scale differ materially from Deep Space 1 and from generation-ship autonomy.

   Citations

   1. Source ID

      src-ak-nasa-starling-flight-results

      Locator

      Mission description and result sections covering four 6U CubeSats, crosslink networking, StarFOX navigation, ROMEO maneuver planning, distributed science autonomy across three spacecraft, and limitations on the full planned demonstrations.

      Relation

      Direct demonstration (exact value: direct-demonstration)

   2. Source ID

      src-ak-nasa-ds1-remote-agent

      Locator

      Deep Space 1 single-spacecraft Remote Agent experiment scope, selected subsystems, high-level goal planning, simulated-fault set, duration, and ground-team role.

      Relation

      Context only (exact value: context-only)

   3. Source ID

      src-mp-nasa-se-handbook

      Locator

      Technology assessment, technical-performance measures, verification, validation, and lifecycle context used to keep demonstrations within their requirements and test boundaries.

      Relation

      Direct method (exact value: direct-method)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   New NASA corrections or primary flight data that materially revise Starling's spacecraft count, completed objectives, autonomy scope, or stated limitations would change this assessment. A larger or longer swarm demonstration would raise readiness for its declared tasks but would remain a separate evidence class from habitat governance and maintenance.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. spacecraft-safety

5. claim · claim-11-04

   LLMs may serve bounded advisory uses such as retrieval, tutoring, translation, incident summarization, or candidate plans only with approved source revisions, exact locators, conflict handling, abstention, and independent authority. A signature can establish integrity and authenticity, not truth, currency, authorization, or safety.

   Record fingerprint

   56fe8de4eacc7361e91be29cd7d3acd32945264b2202031883139d03e96a065b

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   child-rights, culture, education, governance, privacy, surveillance

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-04

   System Slug

   ai-autonomy

   Kind

   current state

   Statement Ref

   Field

   currentState

   Index

   2

   Statement

   LLMs may serve bounded advisory uses such as retrieval, tutoring, translation, incident summarization, or candidate plans only with approved source revisions, exact locators, conflict handling, abstention, and independent authority. A signature can establish integrity and authenticity, not truth, currency, authorization, or safety.

   Statement Fingerprint

   8430cc65711f660fb33fc765eb87afa4f37b2486d9cbe0210ab0923d1ddc7500

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   proposed

   Readiness

   Early research (exact value: early-research)

   Confidence

   supported

   Rationale

   Generative-AI and education guidance documents retrieval, tutoring, translation, confabulation, privacy, bias, provenance, and human-agency considerations. No cited evidence validates an LLM as independent authority for safety, education, culture, or governance.

   Citations

   1. Source ID

      src-pa-nist-ai-600-1

      Locator

      Confabulation, information integrity, privacy, harmful bias, provenance, human-AI configuration, evaluation, and incident-disclosure risks and actions.

      Relation

      Direct normative authority (exact value: direct-normative-authority)

   2. Source ID

      src-cu-unesco-genai-education

      Locator

      Chapters 2–4 and policy framework, human agency, inclusion, linguistic diversity, data protection, age, validation, and educational purpose.

      Relation

      Direct normative authority (exact value: direct-normative-authority)

   3. Source ID

      src-pn-ccsds-oais

      Locator

      Sections 2–4, source content, representation information, provenance, context, fixity, access, and designated-community concepts.

      Relation

      Context only (exact value: context-only)

   4. Source ID

      src-cu-who-unicef-assistive-technology

      Locator

      Executive summary and chapters on people, products, provision, personnel, policy, services, and barriers to equitable access.

      Relation

      Context only (exact value: context-only)

   5. Source ID

      src-cu-w3c-wcag22

      Locator

      Perceivable, operable, understandable, and robust principles, success criteria, conformance requirements, and technology-neutral scope.

      Relation

      Context only (exact value: context-only)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work that may include offline AI support; no model vendor, school, archive, customer, sponsor, or partner relationship currently exists.

   What Would Change

   Independent evaluations showing that a broader LLM authority model provides equal or stronger provenance, privacy, abstention, error detection, cultural plurality, child protection, appeal, and safe AI-off fallback could revise these boundaries.

   High Consequence

   1. education
   2. privacy
   3. surveillance
   4. culture
   5. governance
   6. child-rights

6. claim · claim-11-05

   Digital-twin methods support specific testing and operational tasks within declared validity envelopes; every model is partial and may diverge as physical systems, configurations, and environments change.

   Record fingerprint

   ecccb3fd9bc72f211c52006694627970e079c0285b2fa385aa7354f895940e80

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, life-support-continuity, modeling-simulation

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-05

   System Slug

   ai-autonomy

   Kind

   current state

   Statement Ref

   Field

   currentState

   Index

   3

   Statement

   Digital-twin methods support specific testing and operational tasks within declared validity envelopes; every model is partial and may diverge as physical systems, configurations, and environments change.

   Statement Fingerprint

   72f48a63abfb83e87388f563ad3c0f497ffb27d7a6ea49936ee414664f83bb6e

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   observed

   Readiness

   Major scale up (exact value: major-scale-up)

   Confidence

   strong

   Rationale

   NASA's active modeling-and-simulation standard requires intended use, requirements, credibility products, verification, validation, uncertainty, configuration, and acceptance. NIST describes digital-twin operations plus security and trust concerns, while NASA's foundational paper presents a lifecycle concept. Together they support bounded utility and the expectation of divergence as systems, data, and environments change.

   Citations

   1. Source ID

      src-ak-nasa-models-simulations-7009b

      Locator

      Sections 1 through 5 on intended use, M&S requirements, lifecycle, credibility products, verification, validation, uncertainty, configuration management, use assessment, and acceptance.

      Relation

      Direct method (exact value: direct-method)

   2. Source ID

      src-ak-nist-digital-twin-8356

      Locator

      Sections 2 through 6 on concepts, components, operations, scenarios, and applications, and sections 7 and 8 on cybersecurity and trust considerations.

      Relation

      Direct observation (exact value: direct-observation)

   3. Source ID

      src-ak-nasa-digital-twin-2012

      Locator

      Digital-twin paradigm, integrated models and vehicle data, lifecycle vision, and future-research framing.

      Relation

      Context only (exact value: context-only)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   Long-duration evidence that a digital twin maintains independently measured and calibrated error bounds through sensor drift, hardware substitution, software migration, environmental change, adversarial data, and changing operators would narrow the divergence claim. Undetected out-of-envelope use or common-mode validation failures would strengthen it.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. modeling-simulation
   4. life-support-continuity

7. claim · claim-11-06

   This foundation corpus contains no published demonstration of a generative or autonomous system maintaining safe, legitimate judgment for a diverse crew across generations.

   Record fingerprint

   fe5dbd4bf05ae0a9f0ab04b432ded2e149b963bd465b088f67bad33c161b5d48

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, governance, human-rights, life-support-continuity

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-06

   System Slug

   ai-autonomy

   Kind

   unknown

   Statement Ref

   Field

   unknowns

   Index

   0

   Statement

   This foundation corpus contains no published demonstration of a generative or autonomous system maintaining safe, legitimate judgment for a diverse crew across generations.

   Statement Fingerprint

   665b8c95ef2d0f82efd58c8074fc4d3dc7b3941abf983fc8ecdcc3aaa18a48ec

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   observed

   Readiness

   No known path (exact value: no-known-path)

   Confidence

   supported

   Rationale

   The bounded primary corpus contains finite spacecraft autonomy demonstrations, current AI risk frameworks, and normative ethics guidance. It contains no demonstration of a generative or autonomous system maintaining safe and legitimate judgment for a diverse crew across generations. This is a bounded-corpus finding rather than a universal proof of absence.

   Citations

   1. Source ID

      src-ak-nasa-ds1-remote-agent

      Locator

      Finite Remote Agent flight-experiment scope, selected subsystems, simulated-fault set, timing bug, pause, and ground-team involvement.

      Relation

      Direct demonstration (exact value: direct-demonstration)

   2. Source ID

      src-ak-nasa-starling-flight-results

      Locator

      Finite four-CubeSat technology-demonstration architecture, task-specific autonomy results across three spacecraft, and reported mission limitations.

      Relation

      Direct demonstration (exact value: direct-demonstration)

   3. Source ID

      src-pa-nist-ai-600-1

      Locator

      Current generative-AI risks and management actions, including confabulation, privacy, information integrity, human-AI configuration, and value-chain concerns.

      Relation

      Scope boundary (exact value: scope-boundary)

   4. Source ID

      src-ak-unesco-ai-ethics

      Locator

      Human dignity, rights, proportionality, safety, fairness, privacy, oversight, responsibility, transparency, governance, and ethical-impact provisions.

      Relation

      Scope boundary (exact value: scope-boundary)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   A published, independently audited multigenerational demonstration involving changing people, institutions, hardware, models, conflicts, rights, and life-safety decisions would falsify this bounded finding if it actually established safe and legitimate judgment. Short benchmarks, dialogue quality, or isolated technical autonomy would not.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. governance
   4. human-rights
   5. life-support-continuity

8. claim · claim-11-07

   Confabulation, invented citations, prompt or tool injection, poisoned procedures or telemetry, stale signed material, automation bias, privacy leakage, evaluator contamination, sensor spoofing, and correlated model/runtime failure threaten epistemic resilience.

   Record fingerprint

   768bb2389d46e55e38419c092772ff24a490b471c33fed16d5351f70c3fb3f7c

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, dual-use-risk, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, dual-use, epistemic-resilience, privacy

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-07

   System Slug

   ai-autonomy

   Kind

   unknown

   Statement Ref

   Field

   unknowns

   Index

   1

   Statement

   Confabulation, invented citations, prompt or tool injection, poisoned procedures or telemetry, stale signed material, automation bias, privacy leakage, evaluator contamination, sensor spoofing, and correlated model/runtime failure threaten epistemic resilience.

   Statement Fingerprint

   f9af4ea4bf1752210948601b3d5cadda2463ae7943e95fb326b42da49b1930e8

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   observed

   Readiness

   Early research (exact value: early-research)

   Confidence

   strong

   Rationale

   NIST's GenAI Profile and adversarial-ML taxonomy directly document confabulation, information-integrity, privacy, poisoning, evasion, and misuse risks and their mitigation limits. Digital-twin and secure-development guidance adds sensor, data, component, toolchain, lifecycle, and correlated dependency concerns. Application to multigenerational epistemic resilience remains a systems inference.

   Citations

   1. Source ID

      src-pa-nist-ai-600-1

      Locator

      Risk sections on confabulation, data privacy, information integrity, human-AI configuration, value chains, and related governance, measurement, and incident actions.

      Relation

      Direct observation (exact value: direct-observation)

   2. Source ID

      src-cr-nist-aml-100-2e2025

      Locator

      Taxonomy chapters for predictive- and generative-AI poisoning, evasion, privacy, misuse, lifecycle stages, attacker capabilities, and mitigation limitations.

      Relation

      Direct observation (exact value: direct-observation)

   3. Source ID

      src-ak-nist-ssdf-ai-800218a

      Locator

      AI-specific secure-development additions addressing model, data, code, dependency, evaluation, release, provenance, and vulnerability-response practices.

      Relation

      Direct observation (exact value: direct-observation)

   4. Source ID

      src-ak-nist-digital-twin-8356

      Locator

      Sections 7 and 8 on digital-twin cybersecurity, trust, sensors, connections, data, components, operations, and accepted-quality concerns.

      Relation

      Direct observation (exact value: direct-observation)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   Representative end-to-end evaluations should separately measure each risk, severe outcomes, correlated failures, operator calibration, privacy loss, abstention, and recovery under offline conditions. Strong replicated evidence that a named risk is inapplicable to the specified architecture would narrow the enumeration; a safety prompt or average benchmark would not.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. privacy
   4. dual-use
   5. epistemic-resilience

9. claim · claim-11-08

   Robots still lack the broad manipulation, diagnosis, fabrication, and self-repair needed to maintain a worldship.

   Record fingerprint

   8e0a459bb7f93a19fc0cacea0dc2b7eb61b1dcfbe408d3a0ec75190c2caf09fb

   Minimum approvals

   2

   Required scope groups

   domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

   High-consequence domains

   ai-autonomy, cybersecurity, life-support-continuity, manufacturing, spacecraft-safety

   Review state

   pending

   Published human decisions

   0

   Inspect the complete frozen review surface

   ID

   claim-11-08

   System Slug

   ai-autonomy

   Kind

   unknown

   Statement Ref

   Field

   unknowns

   Index

   2

   Statement

   Robots still lack the broad manipulation, diagnosis, fabrication, and self-repair needed to maintain a worldship.

   Statement Fingerprint

   4115a812d070972b7c5fb82af5c8c45179ca417d682abe4bf564d18c85e655df

   Assessment

   Status

   Editorial assessed (exact value: editorial-assessed)

   Basis

   observed

   Readiness

   Breakthrough dependent (exact value: breakthrough-dependent)

   Confidence

   strong

   Rationale

   NASA and GAO surveys describe task-specific servicing, inspection, fabrication, assembly, robotics, and autonomy efforts while documenting limited demonstrations and separate development paths. No cited system integrates broad manipulation, diagnosis, fabrication, qualification, installation, and self-repair sufficient to maintain a worldship.

   Citations

   1. Source ID

      src-im-nasa-isam-2025

      Locator

      Capability taxonomy and state-of-play survey for inspection, servicing, repair, assembly, manufacturing, construction, robotics, autonomy, and present demonstrations.

      Relation

      Direct observation (exact value: direct-observation)

   2. Source ID

      src-im-nasa-ism-portfolio-2025

      Locator

      Separate portfolio paths for polymers, metals, electronics, welding, recycling, biomanufacturing, verification, autonomy, and development status.

      Relation

      limitation

   3. Source ID

      src-im-gao-isam-2025

      Locator

      Pages 8 through 24 on demonstrated servicing, lower manufacturing maturity, robotic and test limitations, qualification, standards, adoption barriers, and program dependencies.

      Relation

      Direct observation (exact value: direct-observation)

   Context Source IDs

   1. core-11-1
   2. core-11-2
   3. core-11-3
   4. core-11-4

   Editorial Provenance

   Status

   Substantive editorial review (exact value: substantive-editorial-review)

   Reviewers

   1. GShips Project editorial synthesis

   Conflicts

   1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

   What Would Change

   An independently replicated robotic system that diagnoses unfamiliar failures, manipulates diverse damaged hardware, produces and qualifies replacements, installs them, restores its own tools and sensors, and repeats under realistic habitat constraints would raise readiness. Narrow demonstrations would change only their declared task boundaries.

   High Consequence

   1. ai-autonomy
   2. cybersecurity
   3. spacecraft-safety
   4. manufacturing
   5. life-support-continuity

10. claim · claim-11-09

    Bounded offline operations support, digital-twin fault ranges, knowledge arks, source provenance, skill retention, and AI-off drills may benefit remote industry, disaster response, and long-lived institutions when their limits are independently evaluated.

    Record fingerprint

    de198a3912f3c2315c4cbf9edf97b6e782e38a95404da605e1f57c895c550804

    Minimum approvals

    2

    Required scope groups

    domain-method: ai-knowledge-assurance, defensive-cyber-safety, governance-law-rights; rights-public-interest: affected-public-rights

    High-consequence domains

    cybersecurity, education, governance, labor, privacy, surveillance

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    claim-11-09

    System Slug

    ai-autonomy

    Kind

    earth benefit

    Statement Ref

    Field

    earthBenefits

    Index

    0

    Statement

    Bounded offline operations support, digital-twin fault ranges, knowledge arks, source provenance, skill retention, and AI-off drills may benefit remote industry, disaster response, and long-lived institutions when their limits are independently evaluated.

    Statement Fingerprint

    fe64b827c9316cbc010f7fcce9ebf4e955689bcb6e5c90c9311f58a8145533a9

    Assessment

    Status

    Editorial assessed (exact value: editorial-assessed)

    Basis

    proposed

    Readiness

    Early research (exact value: early-research)

    Confidence

    tentative

    Rationale

    Archival, apprenticeship, delay-tolerant networking, and AI-risk frameworks support bounded offline retrieval, local continuity, provenance, and recovery exercises. Their combined benefit to remote industry and long-lived institutions requires evaluated deployments rather than extrapolation.

    Citations

    1. Source ID

       src-pa-nist-ai-600-1

       Locator

       Generative-AI governance, provenance, evaluation, confabulation, privacy, information-integrity, security, and human-AI risk actions.

       Relation

       Direct normative authority (exact value: direct-normative-authority)

    2. Source ID

       src-pn-nasa-dtn

       Locator

       Store-and-forward architecture, disruption and delay use cases, space implementation, and communication boundary.

       Relation

       Context only (exact value: context-only)

    3. Source ID

       src-pn-ietf-bpv7

       Locator

       Sections 1–5, delay-tolerant bundle architecture, blocks, processing, endpoint behavior, reporting, and assumptions.

       Relation

       Direct normative authority (exact value: direct-normative-authority)

    4. Source ID

       src-cu-ilo-r208

       Locator

       Structured learning, qualified supervision, inclusion, safety, assessment, recognition, compensation, and apprentice protections.

       Relation

       Context only (exact value: context-only)

    Context Source IDs

    1. core-11-1
    2. core-11-2
    3. core-11-3
    4. core-11-4

    Editorial Provenance

    Status

    Substantive editorial review (exact value: substantive-editorial-review)

    Reviewers

    1. GShips Project editorial synthesis

    Conflicts

    1. Publisher intends to explore a commercial venture based on some GShips work that may include offline operations support; no operator, AI vendor, customer, sponsor, or partner relationship currently exists.

    What Would Change

    Prospective deployments showing no measurable improvement in local recovery, source use, skill retention, privacy, or service continuity—or unacceptable automation dependence and surveillance—would weaken or reverse this benefit claim.

    High Consequence

    1. education
    2. labor
    3. privacy
    4. surveillance
    5. cybersecurity
    6. governance

11. claim · claim-11-10

    Require independently assessed safety properties, separate physical protection layers, explicit authority gates, tested workload and timing bounds, audited provenance, bounded abstention, manual operation, recovery drills, and independent appeal.

    Record fingerprint

    4c48cfe2109462b8663ca6f17458b61b4867bd93f22b1e45b23700765e45589f

    Minimum approvals

    2

    Required scope groups

    domain-method: ai-knowledge-assurance, defensive-cyber-safety, dual-use-risk, governance-law-rights; rights-public-interest: affected-public-rights

    High-consequence domains

    ai-autonomy, cybersecurity, dual-use, governance, life-support-continuity, privacy

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    claim-11-10

    System Slug

    ai-autonomy

    Kind

    decision gate

    Statement Ref

    Field

    gate

    Statement

    Require independently assessed safety properties, separate physical protection layers, explicit authority gates, tested workload and timing bounds, audited provenance, bounded abstention, manual operation, recovery drills, and independent appeal.

    Statement Fingerprint

    beea132c796130a4d546c31fc0fbc4b09af6be86340d99bd67b05ccf17fadba6

    Assessment

    Status

    Editorial assessed (exact value: editorial-assessed)

    Basis

    normative

    Readiness

    Early research (exact value: early-research)

    Confidence

    supported

    Rationale

    The gate synthesizes present software-safety, modeling-credibility, AI-risk, generative-risk, secure-development, and systems-engineering methods. Physical protection, independent assessment, explicit authority, provenance, workload bounds, abstention, manual operation, recovery, and appeal are separable safeguards; no cited source validates them as one generation-ship assurance case.

    Citations

    1. Source ID

       src-ak-nasa-software-assurance-87398b

       Locator

       Lifecycle software assurance and safety requirements covering objective evidence, security, requirements mapping, independence, IV&V, analysis, testing, maintenance, and retirement.

       Relation

       Direct method (exact value: direct-method)

    2. Source ID

       src-ak-nasa-models-simulations-7009b

       Locator

       Intended-use, requirements, credibility, verification, validation, uncertainty, configuration, acceptance, and results-communication requirements for models and simulations.

       Relation

       Direct model (exact value: direct-model)

    3. Source ID

       src-ak-nist-ai-rmf-100-1

       Locator

       Trustworthy characteristics and Govern, Map, Measure, Manage functions, including human-AI configuration, evaluation context, risk tolerance, monitoring, and accountability.

       Relation

       Direct method (exact value: direct-method)

    4. Source ID

       src-pa-nist-ai-600-1

       Locator

       Confabulation, privacy, information integrity, human-AI configuration, value-chain risk, measurement, red-team, incident, and disclosure actions.

       Relation

       Direct method (exact value: direct-method)

    5. Source ID

       src-mp-nasa-se-handbook

       Locator

       Requirements, interfaces, verification, validation, configuration management, technical risk, decision analysis, and lifecycle review processes.

       Relation

       Direct method (exact value: direct-method)

    Context Source IDs

    1. core-11-1
    2. core-11-2
    3. core-11-3
    4. core-11-4

    Editorial Provenance

    Status

    Substantive editorial review (exact value: substantive-editorial-review)

    Reviewers

    1. GShips Project editorial synthesis

    Conflicts

    1. Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.

    What Would Change

    A legitimate, independently reviewed assurance framework could replace this gate if it demonstrates equal or stronger physical containment, authority, timing, workload, provenance, abstention, AI-off operation, local recovery, rights, and appeal. Repeated representative failures under injected faults should strengthen or narrow individual requirements, not waive them.

    High Consequence

    1. ai-autonomy
    2. cybersecurity
    3. governance
    4. privacy
    5. dual-use
    6. life-support-continuity

12. claim-source · src-ak-nasa-digital-twin-2012

    The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles↗ (opens external site in a new tab)

    Record fingerprint

    7dc587fc2cc5f55374f633dae2558909763aeb1c1337e479fd947424badc95ad

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nasa-digital-twin-2012

    Title

    The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles

    Authors

    1. Edward H. Glaessgen
    2. David S. Stargel

    Publisher

    NASA Technical Reports Server

    Year

    2012

    URL

    https://ntrs.nasa.gov/citations/20120008178↗ (opens external site in a new tab)

    Kind

    Government concept paper (exact value: government-concept-paper)

    Checked At

    2026-07-25

    Scope Note

    Early digital-twin concept integrating models and vehicle data across a lifecycle, presented as a future paradigm and research direction rather than an operational certification.

13. claim-source · src-ak-nasa-ds1-remote-agent

    Deep Space 1: Autonomous Remote Agent↗ (opens external site in a new tab)

    Record fingerprint

    ec23247e17991e05a5e88a376fd8f21d79009c4d5dff22e7cc230af82f876ac1

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nasa-ds1-remote-agent

    Title

    Deep Space 1: Autonomous Remote Agent

    Authors

    1. NASA Jet Propulsion Laboratory

    Publisher

    NASA/JPL

    Year

    1999

    URL

    https://www.jpl.nasa.gov/nmp/ds1/tech/autora.html↗ (opens external site in a new tab)

    Kind

    Official flight experiment record (exact value: official-flight-experiment-record)

    Checked At

    2026-07-25

    Scope Note

    Official account of bounded onboard planning, execution, selected-subsystem control, four simulated faults, a timing bug, experiment pause, and ground-team involvement. Browser-accessible official content returns HTTP 403 to automated checks.

14. claim-source · src-ak-nasa-software-assurance-87398b

    Software Assurance and Software Safety Standard↗ (opens external site in a new tab)

    Record fingerprint

    d6427b989658379d4754ab20c93d5cf9535fe23c6ca356c7d311a285029d3140

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nasa-software-assurance-87398b

    Title

    Software Assurance and Software Safety Standard

    Authors

    1. National Aeronautics and Space Administration

    Publisher

    NASA

    Year

    2022

    URL

    https://standards.nasa.gov/standard/NASA/NASA-STD-87398↗ (opens external site in a new tab)

    Kind

    Active software assurance standard (exact value: active-software-assurance-standard)

    Checked At

    2026-07-25

    Scope Note

    NASA lifecycle requirements for software assurance, software safety, security, objective evidence, requirements mapping, independent verification and validation, maintenance, and retirement.

15. claim-source · src-ak-nasa-starling-flight-results

    Starling CubeSat Swarm Technology Demonstration Flight Results↗ (opens external site in a new tab)

    Record fingerprint

    c420a1d5ded26b43b4f0db1a9c8c6b965addc96d75026b3ad381bfaa88cd7a77

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nasa-starling-flight-results

    Title

    Starling CubeSat Swarm Technology Demonstration Flight Results

    Authors

    1. NASA Starling Project Team

    Publisher

    NASA Technical Reports Server

    Year

    2024

    URL

    https://ntrs.nasa.gov/citations/20240006994↗ (opens external site in a new tab)

    Kind

    Official flight demonstration report (exact value: official-flight-demonstration-report)

    Checked At

    2026-07-25

    Scope Note

    Mission architecture and bounded results for networking, optical navigation, autonomous maneuver planning, and distributed science autonomy across a four-CubeSat demonstration.

16. claim-source · src-ak-nist-ai-rmf-100-1

    Artificial Intelligence Risk Management Framework (AI RMF 1.0)↗ (opens external site in a new tab)

    Record fingerprint

    44ed404a35aadd12a293b2b0f7e8939ef43b5d3c4114c9d456aaec429fd5e411

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nist-ai-rmf-100-1

    Title

    Artificial Intelligence Risk Management Framework (AI RMF 1.0)

    Authors

    1. Elham Tabassi

    Publisher

    NIST

    Year

    2023

    URL

    https://doi.org/10.6028/NIST.AI.100-1↗ (opens external site in a new tab)

    Kind

    Government ai risk management framework (exact value: government-ai-risk-management-framework)

    Checked At

    2026-07-25

    Scope Note

    Voluntary sociotechnical AI risk framework defining trustworthy characteristics and the Govern, Map, Measure, and Manage functions; not a generation-ship assurance standard.

17. claim-source · src-ak-nist-ssdf-ai-800218a

    Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile↗ (opens external site in a new tab)

    Record fingerprint

    83d3d0fd2357e1ad2ee1db05b3a6d25921c7e4abd3125132831fff5213ef2148

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-nist-ssdf-ai-800218a

    Title

    Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile

    Authors

    1. Harold Booth
    2. Murugiah Souppaya
    3. Apostol Vassilev
    4. Michael Ogata
    5. Martin Stanley
    6. Karen Scarfone

    Publisher

    NIST

    Year

    2024

    URL

    https://doi.org/10.6028/NIST.SP.800-218A↗ (opens external site in a new tab)

    Kind

    Government ai secure development guidance (exact value: government-ai-secure-development-guidance)

    Checked At

    2026-07-25

    Scope Note

    AI-specific additions to secure-development practices for organizational preparation, artifact protection, well-secured production, evaluation, release, and vulnerability response.

18. claim-source · src-ak-unesco-ai-ethics

    Recommendation on the Ethics of Artificial Intelligence↗ (opens external site in a new tab)

    Record fingerprint

    5df6900912cc9c2a8b02845211ccf6dff726220b3600e353bb6eaf2e75f0767d

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-ak-unesco-ai-ethics

    Title

    Recommendation on the Ethics of Artificial Intelligence

    Authors

    1. UNESCO General Conference

    Publisher

    UNESCO

    Year

    2021

    URL

    https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence?hub=1063↗ (opens external site in a new tab)

    Kind

    Intergovernmental normative recommendation (exact value: intergovernmental-normative-recommendation)

    Checked At

    2026-07-25

    Scope Note

    Normative values, principles, and policy actions concerning human dignity, rights, proportionality, safety, fairness, privacy, oversight, responsibility, transparency, governance, and ethical impact.

19. claim-source · src-cr-nist-aml-100-2e2025

    Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations↗ (opens external site in a new tab)

    Record fingerprint

    1573c29a25a7b8302f31f3a676e7e80866b6ff58e0c0718e60a38f52ecbd3e5a

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-cr-nist-aml-100-2e2025

    Title

    Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

    Authors

    1. Apostol Vassilev
    2. Alina Oprea
    3. Alie Fordyce
    4. Hyrum Anderson
    5. Xander Davies
    6. Maia Hamin

    Publisher

    NIST

    Year

    2025

    URL

    https://doi.org/10.6028/NIST.AI.100-2e2025↗ (opens external site in a new tab)

    Kind

    Government ai security taxonomy (exact value: government-ai-security-taxonomy)

    Checked At

    2026-07-25

    Scope Note

    Predictive- and generative-AI evasion, poisoning, privacy, and misuse taxonomy, lifecycle stages, attacker capabilities, mitigations, and limitations.

20. claim-source · src-cu-unesco-genai-education

    Guidance for Generative AI in Education and Research↗ (opens external site in a new tab)

    Record fingerprint

    ed1746a19077b3ecd178cc5bfff679bf94370ef3e3038770e28b28e0fb515e99

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-cu-unesco-genai-education

    Title

    Guidance for Generative AI in Education and Research

    Authors

    1. United Nations Educational, Scientific and Cultural Organization

    Publisher

    UNESCO

    Year

    2023

    URL

    https://unesdoc.unesco.org/ark:/48223/pf0000386693↗ (opens external site in a new tab)

    Kind

    Un education and ai guidance (exact value: un-education-and-ai-guidance)

    Checked At

    2026-07-25

    Scope Note

    Human agency, inclusion, linguistic and cultural diversity, data protection, age appropriateness, validation, governance, and educational-use guidance.

21. claim-source · src-cu-w3c-wcag22

    Web Content Accessibility Guidelines 2.2↗ (opens external site in a new tab)

    Record fingerprint

    36d83ac5a6eceb59dcdfb30918e2154e158d015fa3c29031673845a594599b7e

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-cu-w3c-wcag22

    Title

    Web Content Accessibility Guidelines 2.2

    Authors

    1. World Wide Web Consortium

    Publisher

    W3C

    Year

    2023

    URL

    https://www.w3.org/TR/WCAG22/↗ (opens external site in a new tab)

    Kind

    International web accessibility standard (exact value: international-web-accessibility-standard)

    Checked At

    2026-07-25

    Scope Note

    Technology-neutral principles, guidelines, success criteria, conformance requirements, and limits for perceivable, operable, understandable, and robust web content.

22. claim-source · src-cu-who-unicef-assistive-technology

    Global Report on Assistive Technology↗ (opens external site in a new tab)

    Record fingerprint

    8453de49aa65cf960e18570b9c038ea2a575728ec37a5a198392e3abd1c6bb36

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-cu-who-unicef-assistive-technology

    Title

    Global Report on Assistive Technology

    Authors

    1. World Health Organization
    2. United Nations Children's Fund

    Publisher

    World Health Organization

    Year

    2022

    URL

    https://www.who.int/publications/i/item/9789240049451↗ (opens external site in a new tab)

    Kind

    Un global health evidence report (exact value: un-global-health-evidence-report)

    Checked At

    2026-07-25

    Scope Note

    Global evidence and recommendations concerning assistive-technology access, people, products, provision, personnel, policy, services, and persistent access gaps.

23. claim-source · src-pn-ietf-bpv7

    RFC 9171: Bundle Protocol Version 7↗ (opens external site in a new tab)

    Record fingerprint

    2cc953af46ae7cc31aca7ff9b9cba37bc0bdc5a6f045b41a54e790affdba4395

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-pn-ietf-bpv7

    Title

    RFC 9171: Bundle Protocol Version 7

    Authors

    1. Scott Burleigh
    2. Kevin Fall
    3. Edward Birrane

    Publisher

    Internet Engineering Task Force

    Year

    2022

    URL

    https://www.rfc-editor.org/rfc/rfc9171.html↗ (opens external site in a new tab)

    Kind

    Internet standard (exact value: internet-standard)

    Checked At

    2026-07-25

    Scope Note

    Delay-tolerant networking architecture, bundle format, node processing, endpoint behavior, and explicit disrupted-network assumptions.

24. claim-source · src-pn-nasa-dtn

    Delay/Disruption Tolerant Networking↗ (opens external site in a new tab)

    Record fingerprint

    3940190c4eaa98e8cbef9e104dffd5f111bb61b614373849ee0b327c593d7e75

    Minimum approvals

    1

    Required scope groups

    bounded-competence: information-science

    High-consequence domains

    None under the named two-person rule

    Review state

    pending

    Published human decisions

    0

    Inspect the complete frozen review surface

    ID

    src-pn-nasa-dtn

    Title

    Delay/Disruption Tolerant Networking

    Authors

    1. National Aeronautics and Space Administration

    Publisher

    NASA

    Year

    2026

    URL

    https://www.nasa.gov/communicating-with-missions/delay-disruption-tolerant-networking/↗ (opens external site in a new tab)

    Kind

    Active technology and operations record (exact value: active-technology-and-operations-record)

    Checked At

    2026-07-25

    Scope Note

    Store-and-forward bundle operation, mission uses, High-Rate DTN testing, and bounded terrestrial and space-network applicability.

Frozen source snapshots

Source inclusion does not determine the disposition. Reviewers must inspect the cited locator and relation, note inaccessible material, and identify stronger or conflicting evidence.

Offline packet and worksheet

Downloads contain no reviewer contact details. Downloading does not create an account or store a review response in the GShips application. Ordinary provider request or analytics logs may record the download request. Work locally: the public site has no review account, upload endpoint, or decision-submission API.

Do not paste a completed decision, identity documents, private contact data, confidential conflict evidence, medical information, controlled material, or exploit details into a public form. Until a separately authorized private handoff exists, retain the completed worksheet locally.

Packet schema · JSON · Decision-bundle schema · JSON

Validate offline

Use Node.js 22.13.0 or later. Keep the packet, worksheet, completed decision, and all six kit files together in a local directory.

1. Download the six kit files below. Complete a copy of the JSON template offline and preserve its templateFingerprint.
2. Finalize a separate output file.

   node finalize-review-decision.mjs \
     --input DRAFT.json \
     --output COMPLETED.json

   This marks the copy complete and calculates an unkeyed canonical bundle fingerprint. A fingerprint detects changes; it is not a reviewer signature.

3. Validate the packet and completed copy.

   node check-review-decisions.mjs \
     --packet PACKET.json \
     --decision COMPLETED.json

   Add another --decision for each independent reviewer.

4. Interpret the result narrowly. A zero exit proves structural consistency only. Neither command appoints or qualifies a reviewer, establishes independence, accepts a decision, or authorizes publication.

• Bundle finalizer
• Validator command
• Decision policy module
• Fingerprint and packet-integrity module
• Review-domain policy module
• Canonical synthesis and interactive review specs