Development of a Fatigue Risk Management System for the Canadian Aviation Industry
Bibliographic record
Abstract
This report details the work done in the development of a fatigue risk management system (FRMS) for the Canadian aviation industry. This performance-based system aims to reduce fatigue-related errors and provide more flexibility for operators in the way they deal with these risks. Work focused on the development of an FRMS toolbox. The key elements of the toolbox are policy and procedures guidelines to tailor the FRMS to specific operational requirements, competency-based training and education materials, and audit methodologies to identify both risk level and whether interventions have been successful in reducing risk. Extensive industry consultation was undertaken to ensure the FRMS toolbox would meet the needs of operators. Feedback from stakeholders was taken into consideration throughout the development of the toolbox and is also reflected in the recommendations for FRMS policy, implementation and assessment. Stakeholders believe that considerable assistance from Transport Canada will be required to effectively implement an FRMS. Cultural change, particularly at the individual level, also remains a significant obstacle to FRMS acceptance. However, with sufficient preparedness and strong communication, the aviation industry and Transport Canada should be able to overcome these hurdles and work collaboratively to better manage fatigue-related risk.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".