General Recommendations on Fatigue Risk Management for the Canadian Forces
Bibliographic record
Abstract
Abstract : A recent Advisory Publication (ADV PUB Number ASMG 6000, 7 Jan 2010) on Fatigue Countermeasures in Sustained and Continuous Operations recommended that all Air and Space Interoperability Council (ASIC) nations should have national policies regarding fatigue management. Currently, there is no existing doctrine and training program for fatigue risk management available in the Canadian forces (CF). The focus of this document is on the management of sleep hygiene and circadian entrainment, rather than physical, muscle fatigue, or fatigue at the cellular level. Recommendations for fatigue management are based on best practices derived from the latest scientific findings and the collation of appropriate common policies from other military forces that will enable aircrew to perform at their best. It includes a series of summaries that address what is and what is not known regarding the efficacy, implementation and limitation associated with fatigue countermeasures commonly employed. A stratified approach is adopted to ensure that promotion of sleep is the first priority under routine fatigue management, followed by generally approved pharmacological intervention. Employment of those prescription medications permitted by CF policies will be suggested only as a last resort. This document is written primarily for the Air Force; however, the general recommendations to fatigue risk management also apply to the Navy and the Army as they, too, experience sleep loss due to changing time zones and changing operational schedules. The intended key users for these recommendations include commanders, unit trainers, mission planners, medical officers, unit safety officers, and all personnel who support operations. They are well advised to familiarize themselves with the causes of fatigue and the various options in fatigue risk management. This guide is considered to be a living document. The material will be updated as new technological information and empirical scientific data emerge
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".