Cold operational readiness in the military: from science to practice
Bibliographic record
Abstract
Cold weather operations are logistically difficult to orchestrate and extremely challenging for soldiers. Decades of research and empirical evidence indicate that humans are extremely vulnerable to cold and that individual responses are highly variable. In this context, it may be necessary to develop personalised strategies to sustain soldiers' performance and ensure overall mission success in the cold. Systematic cold weather training is essential for soldiers to best prepare to operate during, and recover from, cold weather operations. The purpose of this review is to highlight key aspects of cold weather training, including (1) human responses to cold, (2) nutrition, (3) sleep and (4) protective equipment requirements. Bringing science to practice to improve training principles can facilitate soldiers performing safely and effectively in the cold. Cold weather training prepares soldiers for operations in cold, harsh environments. However, decreases in physical, psychological and thermoregulatory performance have been reported following such training, which influences operational ability and increases the overall risk of injuries. When optimising the planning of field training exercises or operational missions, it is important to understand the soldiers' physical and cognitive performance capacity, as well as their capacity to cope and recover during and after the exercise or mission. Even though the body is fully recovered in terms of body composition or hormonal concentrations, physical or cognitive performance can still be unrecovered. When overlooked, symptoms of overtraining and risk of injury may increase, decreasing operational readiness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".