Bibliographic record
Abstract
EFFECTS OF AIR POLLUTION ON EYE HEALTH. In addition to its well-known effects on cardio-respiratory health, air pollution could also have an impact on eye health. Indeed, certain air pollutants, especially fine particles and nitrogen dioxide, can enter the bloodstream, triggering oxidative and inflammatory mechanisms involved in the development of the major eye diseases (glaucoma, age-related macular degeneration [AMD], cataracts). Over the past 5 years, some 15 epidemiological studies carried out in Asia, Europe and Canada have consistently shown an increased risk of these diseases in individuals most exposed to air pollution at long term, particularly to fine particles. These results support the lowering of air pollution thresholds recommended by the World Health Organization (WHO) in 2021, which nevertheless continue to be exceeded in most European cities. EFFETS DE LA POLLUTION DE L’AIR SUR LA SANTÉ OCULAIRE. Outre ses répercussions bien connues sur la santé cardiorespiratoire, la pollution atmosphérique pourrait avoir des effets sur la santé oculaire. En effet, certains polluants atmosphériques, notamment les particules fines et le dioxyde d’azote, peuvent pénétrer dans la circulation sanguine, déclenchant des mécanismes oxydatifs et inflammatoires impliqués dans le développement des principales maladies oculaires (glaucome, dégénérescence maculaire liée à l’âge [DMLA], cataracte). Au cours des cinq dernières années, une quinzaine d’études épidémiologiques, réalisées en Asie, en Europe et au Canada, ont montré de manière très cohérente une augmentation du risque de ces maladies chez les personnes les plus exposées au long cours à la pollution atmosphérique, particulièrement aux particules fines. Ces résultats confortent la baisse des seuils de pollution atmosphérique recommandés par l’Organisation mondiale de la santé (OMS) en 2021, qui continuent néanmoins à être dépassés dans la plupart des villes européennes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.445 | 0.328 |
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".