No detectable decrease in extreme cold-related mortality in Canada from Arctic sea ice loss
Bibliographic record
Abstract
Abstract Arctic amplification (AA), the phenomenon by which Arctic surface temperatures are warming faster than the global average, may have significant unexplored impacts on temperature-related mortality in human populations across Canada. We explore the role of Arctic sea ice loss, a key driver of AA, in changing cold temperature extremes across Canada and their impact on human mortality. We use a multi-model ensemble of climate simulations from the Polar Amplification Model Intercomparison Project and a distributed lag nonlinear mortality model in 27 regions covering Canada to quantify the role of Arctic sea ice loss in changing human mortality in the cold season. We find that despite a robust increase in 5th percentile temperatures across eastern Canada, there is no detectable decrease in mortality associated with the most extreme cold, due to mortality in many regions having low sensitivity to warming of cold extremes. The study attributes the temperature-related mortality impact of a physical process, namely Arctic sea ice loss, and highlights Canada’s robust adaptation to extreme cold.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".