Results and methodology for classifying high risk pilots using CANFLY: A cognitive health screening tool for aviators
Bibliographic record
Abstract
Cognitive health screening for aviators would assist in managing a shortage of experienced pilots. Extending pilot careers by optimizing their cognitive health would address both the number and quality of pilots available for airline and general aviation operations. The present work tested the validity of an online screening tool for pilots that measures aviation domain-relevant cognition. Sixty-five licensed pilots (18–80 years, M = 48.8, SD = 16.3) with varying levels of experience completed a 30-min online cognitive health screening tool for pilots. Risk status was determined via a novel metric using self-reported incidents. Machine learning algorithms identified the cognitive factors most useful in identifying pilots with increased risk for accidents and serious incidents. Support vector machines and boosted decision tree algorithms provided the most reliable and strongest classifications models of pilot risk. Findings support the use of this short online screening tool for highlighting performance issues with domain-relevant cognitive abilities based on the Dynamic Mental Model for pilots, such as situation awareness and prospective memory. Understanding personal cognitive challenges is the basis for customized skill maintenance designed to augment cognition for those interested in safely extending their piloting careers.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".