A Language-Oriented Analysis of Situation Awareness in Pilots in High-Fidelity Flight Simulation
Bibliographic record
Abstract
In general aviation (GA), higher critical incident rates have been observed in older pilots as compared to younger pilots.The present research investigates age-related changes in situation awareness (SA) abilities, particularly as it is influenced by the detection and processing of auditory information during a simulated flight.Previous literature, in both aviation psychology and neural auditory processing research, has found older age to be associated with the reduction of both auditory neural resource management and SA abilities.An analysis of radio communication was conducted to determine whether pilot SA is associated with age-related declines in auditory processing and whether certain features of auditory messages may be especially difficult for older pilots to process.The neural auditory pipeline was also inspected using auditory tone stimuli in order to examine at which stages of neural processing do differences in auditory processing arise for older versus younger pilots during flight.Findings showed that aging negatively impacts the integration of aurally presented information into SA efficiency.Additionally, negative age-effects seen in pilot SA abilities may be associated with differences in how information is processed along the auditory neural pipeline.This research is important in informing efforts in creating adaptations of current audiometric pilot testing and training as well as adaptive tools for older pilots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".