Increased winter drownings in ice-covered regions with warmer winters
Bibliographic record
Abstract
Winter activities on ice are culturally important for many countries, yet they constitute a high safety risk depending upon the stability of the ice. Because consistently cold periods are required to form stable and thick ice, warmer winters could degrade ice conditions and increase the likelihood of falling through the ice. This study provides the first large-scale assessment of winter drowning from 10 Northern Hemisphere countries. We documented over 4000 winter drowning events. Winter drownings increased exponentially in regions with warmer winters when air temperatures neared 0°C. The largest number of drownings occurred when winter air temperatures were between -5°C and 0°C, when ice is less stable, and also in regions where indigenous traditions and livelihood require extended time on ice. Rates of drowning were greatest late in the winter season when ice stability declines. Children and adults up to the age of 39 were at the highest risk of winter drownings. Beyond temperature, differences in cultures, regulations, and human behaviours can be important additional risk factors. Our findings indicate the potential for increased human mortality with warmer winter air temperatures. Incorporating drowning prevention plans would improve adaptation strategies to a changing climate.
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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 | 0.001 | 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) | 0.002 | 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".