The effects of outdoor air pollution on chronic illnesses
Bibliographic record
Abstract
Chronic diseases, especially cancer, cardiovascular disease, and respiratory diseases, are the leading causes of morbidity and mortality worldwide.In Canada alone, chronic diseases account for 87% of total disability and 89% of all deaths (1).It is estimated that half of the Canadian population live currently with a chronic illness and more than 90% of them have these three diseases (1).Chronic diseases develop typically over long periods of time and have multiple risk factors.The major causes of cardiovascular disease include high cholesterol, high blood pressure, and obesity (2-6).For lung cancer and respiratory disease, tobacco smoking and exposure to toxic chemicals are important risk factors (7-15).More recently, ambient air pollution has been implicated in increasing the incidence and mortality from lung cancer and from cardio-pulmonary diseases (16-19).Present-day urban air pollution comprises hundreds of substances, including sulphur dioxide, ozone, nitrogen oxide, nitrogen dioxide, carbon monoxide, carbon dioxide, particulate matter, rubber dust, polycyclic aromatic hydrocarbons, and many different volatile organic compounds.Particles are a heterogeneous mixture of solid and liquid droplets with wide distributions of size and mass.Coarse particles, greater than 2.5 µm in median aerodynamic diameter, derive from a variety of sources including windblown dust and grinding operations; fine particles are primarily from the combustion of fossil fuels (20).Common constituents of particulates include elemental and organic carbon, sulphates, nitrates, pollen, microbial contaminants, and metals (20).Fine particles can react with sulphur dioxide and oxides of nitrogen in the atmosphere to form strong acids, such as sulphuric acid, nitric acid, hydrochloric acid, and acid aerosols (20).In addition, urban air also contains benzene and 1,3butadiene that are considered carcinogenic (21).The health impact of outdoor air pollution became apparent during the smog episodes in London, England, (Figure 1) in the 1950s and in some other places.In London, the lethal fog, which occurred because a
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".