Bivariate Extreme Value Analysis of Extreme Temperature and Mortality in Canada, 2000-2020
Bibliographic record
Abstract
Abstract Climate change increases the risk of illness through rising temperature,severe precipitation and worst air pollution. This paper investigates howmonthly excess mortality rate is associated with the increasing frequencyand severity of extreme temperature in Canada during 2000–2020. Theextreme associations were compared among four age groups across fivesub-blocks of Canada based on the datasets of monthly T90 and T10,the two most representative indices of severe weather monitoring mea-sures developed by the actuarial associations in Canada and US. Weutilize a combined seasonal Auto-regressive Integrated Moving Average(ARIMA) and bivariate Peaks-Over-Threshold (POT) method to inves-tigate the extreme association via the extreme tail index χ and Pickandsdependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature withextreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), North-ern Plains (NPL) and Northwest Pacific (NWP) for age group 0–44),while extreme frequent high temperature seems not to affect health signif-icantly (all χ ≤ 0.001 except NWP). Particular attention should be paidto NWP and Central Arctic (CAR) since population health therein ishighly associated with both extreme frequent high and low temperatures(both χ = 0.3182 for all age groups). The revealed extreme depen-dence is expected to help stakeholders avoid significant ramifications withtargeted health protection strategies from unexpected consequences ofextreme weather events. The novel extremal dependence methodology ispromisingly applied in further studies of the interplay between extrememeteorological exposures, social-economic factors and health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".