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Record W4406112588 · doi:10.1080/00140139.2024.2449110

An experimental comparison on the effectiveness of various levels of simulator fidelity on ab initio pilot training

2025· article· en· W4406112588 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueErgonomics · 2025
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFidelitySimulationTraining (meteorology)Computer scienceDriving simulatorAb initioChemistryPhysics

Abstract

fetched live from OpenAlex

Despite recent advances in technology use for education and training, the approach to pilot training over the past several decades has largely remained unchanged. Student pilots complete their training in actual aircraft, with very few flight hours conducted in flight training devices. This study aimed to investigate the effectiveness of various levels of simulator fidelity on ab initio pilot training. Thirty student pilots were invited to train using a virtual reality simulator, desktop simulator, or flight training device. Performance was evaluated using a modified Transport Canada Flight Test Guide alongside the NASA Task Load Index, Subjective Stress Scale, and Simulator Sickness Questionnaire, giving insight into mental workload, stress, and experience of simulator sickness, respectively. Findings show potential for virtual reality and desktop simulators regarding training procedural tasks; however, trainees must be aware of the limitations virtual reality and desktop simulators have concerning the training of aircraft handling tasks.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.320

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)0.0000.000

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 head0.037
GPT teacher head0.294
Teacher spread0.256 · 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