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Record W2029730090 · doi:10.1109/iembs.2010.5627127

Enhancing pilot performance with a SymBodic system

2010· article· en· W2029730090 on OpenAlexaff
Walter Karlen, Sylvain Cardin, Daniël Thalmann, Dario Floreano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSleep deprivationComputer scienceScheduleSleep (system call)SimulationAeronauticsDuration (music)Physical medicine and rehabilitationMedicineEngineeringCircadian rhythmOperating system

Abstract

fetched live from OpenAlex

Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations7
Published2010
Admission routes1
Has abstractyes

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