Bibliographic record
Abstract
On recommendations from 1 Canadian Air Division surgeon, DRDC Toronto received a tasking from 4 Wing to develop models of cognitive effectiveness of CF-18 pilots during Exercise Wolf Safari (an "around the clock" air-to-ground bombing exercise prior to possible deployment of CF-18 aircraft to support the troops in Afghanistan). During work-ups prior to Wolf Safari as well as during the exercise, six CF-18 pilots wore wrist actigraphs for up to 28 days to allow quantification of their daily sleep. Their daily duty times and daily sleep data were inputted to FAST (Trademark) (Fatigue Avoidance Scheduling Tool) to generate models of cognitive assessment for each of the participating pilots. Four of the six pilots showed that the Wolf Safari Op Tempo caused a fatigued-induced impact on modelled cognitive effectiveness similar to or worse than the impact caused by being intoxicated to a blood alcohol level of 0.08%. The remaining two pilots showed a moderate impact on cognitive effectiveness. Some degradation in cognitive effectiveness is inevitable during stressful and complex military operations, especially when conducted at night. To some extent, these performance degradations can be mitigated by ensuring the best possible opportunities for sleep, by sustaining nocturnal alertness with caffeinated gum, and by exploiting the new CF aeromedical policy for the short-term flight supervised prescription of selected sleep-inducing medications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".