Bibliographic record
Abstract
Abstract About 1.5 billion people in developed countries live in urban areas and are chronically exposed to outdoor air pollution, mainly from vehicular and industrial emissions. The WHO forecasts that urban populations will grow fastest in economically disadvantaged countries, comprise 62% of the world’s population by the year 2025, and live in increasingly dense centers of anthropogenic emissions. Over 150 million tons of air pollutants are emitted annually in United States alone. Closely related to population growth and urbanization are the uncertain potential impacts of global climatic change on local, regional, and global air quality. A severe air pollution episode in a river valley in Belgium during 1930 caused several thousand acute respiratory illnesses and about 60 deaths. A similar air pollution episode was associated with several thousand respiratory illness cases and 19 deaths in Donora, Pennsylvania, in 1948 when industrial emissions were trapped in a river valley by a temperature inversion. The severe London smog episode of December 1952 caused about 4000 excess deaths over a 5-day period during which visibility was reduced to as little as 1–5 m. Although most of the excess deaths were from cardiorespiratory diseases among the elderly, death rates doubled among young children.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.305 | 0.192 |
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".