Bibliographic record
Abstract
Abstract In temperate climates, mortality is higher in the winter than the summer. Most wintertime deaths are attributed to cardiovascular and respiratory disease, with hypothermia from extreme cold accounting for a negligible share of all recorded deaths. International and national assessments of the health risks of climate change often conclude that increased temperatures from climate change will likely reduce winter mortality. This article examines the support for this hypothesis. We find that although there is a physiological basis for increased cardiovascular and respiratory disease mortality during winter months, the limited evidence suggests cardiovascular disease mortality is only weakly associated with temperature. Although respiratory disease mortality shows a stronger seasonal relationship with colder temperatures, cold alone does not explain infection rates. Further, respiratory disease mortality is a relatively small proportion of winter deaths. Therefore, assuming no changes in acclimatization and the degree to which temperature‐related deaths are prevented, climate change may alter the balance of deaths between winters and summers, but is unlikely to dramatically reduce overall winter mortality rates. WIREs Clim Change 2013, 4:203–212. doi: 10.1002/wcc.211 This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".