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Record W4403171745 · doi:10.2514/1.a36001

Space Environments Applied to the Gateway Program

2024· article· en· W4403171745 on OpenAlexaboutno aff
Emily M. Willis, Michael L. Goodman, A HENDERSON, Kristin Stillwell, Ron Suggs

Bibliographic record

VenueJournal of Spacecraft and Rockets · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
Fundersnot available
KeywordsGateway (web page)Space (punctuation)Aerospace engineeringComputer scienceEnvironmental sciencePhysicsAeronauticsEngineeringOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

NASA’s Gateway is a crewed space station with a planned mission duration of 15 years in lunar orbit. It is an international collaboration, including partnerships with the European Space Agency, Canadian Space Agency, and Japan Aerospace Explorations Agency. Additionally, the program relies on important partnerships with industry for designing and building hardware. This complex mission poses significant challenges related to space environments. Gateway’s hardware will be directly exposed to the extremes of space weather posing challenges to avionics and materials as well as operations planning. The space environments are defined in the “Design Specification for Natural Environments” and applied through program requirements to all design partners. This paper describes the space environments applicable to the Gateway program, the flow of the environments to system requirements, and the challenges that the program is currently facing with regard to the space environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.220
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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