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Record W3003427826 · doi:10.5772/intechopen.90391

Simulation Fidelity and Skill Learning during Helicopter Egress Training: The Role of Vision

2020· book-chapter· en· W3003427826 on OpenAlex
Stefanie Dawn Martina, Gal Ziv, Elizabeth Sanli, Heather Carnahan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIntechOpen eBooks · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFidelityTraining (meteorology)PsychologyComputer scienceSimulationPhysicsTelecommunicationsMeteorology

Abstract

fetched live from OpenAlex

This project aimed to evaluate the effects of ambient lighting during practice and performance of simulated helicopter escape sequences. Participants were randomized to one of the following groups to practice a standard helicopter underwater escape sequence: Light (with room lights on), Dark (with room lights off), or Graduated (in the light for the first half and then in the dark for the second half of the trials). Following practice, participants had a minimum of 30 min break, followed by retention testing in the dark and then in the light. Dependent measures included accuracy and movement time. Results indicated that participants performed more accurately during the dark retention trial than during the light retention trial. This could be due to increased arousal elicited by performance in the dark or, alternatively, may suggest that performance of helicopter escape sequences is not visually mediated. Based on findings, it appears that training in the light is suitable for potential performance in the dark.

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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.997

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.344
Teacher spread0.310 · 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