Bibliographic record
Abstract
Humans can survive a few minutes without access to air, a few days without water, and a few weeks without food. Even with access to these commodities, if they are contaminated then humans will suffer health problems. This chapter provides insights into the historical context for the eventual development of, and the recognized need for, clean water and clean air regulations, together with an appreciation of some of the technical challenges and political hesitancy that have been encountered in arriving at the modern regulations. Is this important? Yes; despite palpable improvements in water and air quality over the last three decades, estimates suggest that at least 7 million people die prematurely, annually, because of polluted air and contaminated water; at least four times more than the number of lives lost to COVID-19 in 2020, the first year of the global pandemic. Indeed, almost one-third of the global population does not have access to safe drinking water and over 90% live in areas that do not meet the air quality guidelines stipulated by the World Health Organization (WHO). These profound issues seldom receive the same level of attention as topics such as anthropogenic climate change forecasts, water scarcity, and carbon dioxide pollution, albeit students are taught that the global amount of water is inviolate, and that carbon dioxide is a non-contaminating, colorless, odorless, and incombustible gas. How then can there be a scarcity of water and how can a quite unreactive gas cause air pollution? Are humans solely responsible for unsafe water and harmful air? These are simple questions, but the answers are not; they are complex and often disputed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".