Bibliographic record
Abstract
The study of links between environment and health involves examination of those aspects of human health influenced by the physical, social, biological, psychosocial, chemical, economic, political, and cultural environments within which we live, work, and play. Examples abound: the disappearance of the Aral Sea, a fourfold increase in asthma since the mid‐1990s, widespread water‐related illness in the developed world (e.g., Walkerton in Canada, in 2000; Flint, Michigan, in the United States, 2017–present), a doubling of the prevalence of peanut allergy, such high demands for energy that we imperil human safety through the proliferation of nuclear power, which leads to the potential for the proliferation of nuclear weapons in unstable nation‐states. This situation is punctuated by the occurrence of major environmental disasters (Love Canal, Bhopal, Chernobyl). Ongoing concerns about creeping environmental disasters (e.g., the health impacts of global climate change) keep environment and health issues on the front pages of newspapers and at the top of research agendas. The complexity of designing studies that answer targeted questions remains a challenge; and, yet, decisions must be made with respect to policy, regulations, and guidelines designed to protect the health of the public.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.011 |
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".