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Record W2228454803 · doi:10.4271/2008-01-2105

A Parabolic Flight Study of the Mobile Information System

2008· article· en· W2228454803 on OpenAlexaff
Harry L. Litaker, Ronald B. Hoffman, Mihriban Whitmore

Bibliographic record

VenueSAE International Journal of Aerospace · 2008
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsAeronauticsComputer scienceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Focus on sustained lunar operations and human exploration of Mars has become the National Aeronautical Space Administration 's (NASA) goal for the 21st century. NASA's objective is to provide crewmembers with a “hands-free” environment to promote more efficient operations in microgravity and reduced planetary gravity missions. A prototype integrated Mobile Information SysTem (MlST) was evaluated in a simulated microgravity environment while supporting the Langley Research Center (LaRC) Electron Beam Freeform Fabrication (EBF3) experiment. Two LaRC crewmembers wore the MIST during half of the parabolic flights as the experimental group. The other half of the parabolic flights served as a baseline group without the MIST being worn. Post-flight questionnaires, interviews, video/audio analysis, and field observations were used as evaluation measures. Results indicate mobility and compactness of a display are important in experimental procedures and other tasks. Using the MIST facilitated team communications and dynamics capturing more information about the experiment under these flight conditions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.224
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 designSimulation or modeling
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

Citations0
Published2008
Admission routes1
Has abstractyes

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