Bibliographic record
Abstract
Climate change will disturb the Earth's physical systems (e.g.weather patterns) and ecosystems (e.g.disease vector habitats); these disturbances, in turn, will pose direct and indirect risks to human health.Direct risks involve climatic factors that impinge directly on human biology.Indirect risks do not entail direct causal connections between climatic factors and human biology.The Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change elucidates the potential human health impacts of global climate change at both a population and regional level.The impacts on child health, adult health, and the health of the elderly, however, remain largely unexplored.A paucity of research regarding women's health is also extant, despite increasing interest in the issue.According to the TAR, climate change is projected to affect such key issues as air quality, food yields and nutrition, water-related infectious diseases, and water supply.Exposure to cooking fuels, access to food, distribution of food within the family, and choice of water sources is often determined by gender.Thus, women's contributions may, in some cases, make them more vulnerable than their male counterparts to climate change.Moreover, it is anticipated that health care will significantly help people adapt to climate change.Unfortunately, not everyone has adequate health care.In some countries, fewer than 25 % of women visit health-care professionals.Climate change is likely to have a strong, positive (worsening) effect on smog and acidic deposition; climate change is likely to have some effect on suspended particulates.In light of the foregoing, this paper addresses the interrelated and neglected areas of global climate change, air pollution, and women's health.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".