Bibliographic record
Abstract
Amid the bleaker assessments of other contributions, this chapter offers a rare success story, outlining how we have been quicker to mitigate the harm wrought on Earth’s atmosphere. This is, in part, due to the directly visible nature of our impacts: London’s ‘Great Smog’ in 1952; satellite images of a gaping hole in the Ozone layer in the 1980s; the accumulation of ground level ozone, reacting with lead in gasoline fumes, in Los Angeles; acid rain. These indicators led to a series of decisive measures: the UK Clean Air Act in 1956; the banning of CFCs beginning with the Montreal Protocol in 1987; the introduction of catalytic converters; and subsequent amendments to the US Clean Air Act in 1996. These successes aside, the chapter emphasizes the greater threat of air-borne particulate matter that continues to contribute to pollution and is harder to eradicate. This will continue to be an issue as forests are burned, as inefficient fuel usage for cooking in developing nations persists, and as industrial activities – twinned to the inexorable rise of urban populations – continue. The chapter ends on the hope that a combination of technological innovation, transformation of the global energy network and effective public policy can enact the fundamental changes needed to protect our atmosphere.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.325 | 0.215 |
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".