Human vulnerability and variability in the cold: Establishing individual risks for cold weather injuries
Bibliographic record
Abstract
Human tolerance to cold environments is extremely limited and responses between individuals is highly variable. Such physiological and morphological predispositions place them at high risk of developing cold weather injuries [CWI; including hypothermia and/or non-freezing (NFCI) and freezing cold injuries (FCI)]. The present manuscript highlights current knowledge on the vulnerability and variability of human cold responses and associated risks of developing CWI. This review 1) defines and categorizes cold stress and CWI, 2) presents cold defense mechanisms including biological adaptations, acute responses and acclimatization/acclimation and, 3) proposes mitigation strategies for CWI. This body of evidence clearly indicates that all humans are at risk of developing CWI without adequate knowledge and protective equipment. In addition, we show that while body mass plays a key role in mitigating risks of hypothermia between individuals and populations, NFCI and FCI depend mainly on changes in peripheral blood flow and associated decrease in skin temperature. Clearly, understanding the large interindividual variability in morphology, insulation, and metabolism is essential to reduce potential risks for CWI between and within populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".