Bibliographic record
Abstract
The core aim of this paper is to investigate the health impacts of atmospheric particles with aerodynamic diameter of 10 micron or less (PM10) in Makkah. PM10 data were collected by automatic continuous monitoring station in Misfalah, Makkah City. The annual average PM10 concentration during the study period was 195 µg/m3, which is greater than twice the PME standards and 4 times the EC standard. Daily average concentrations also exceeded PME and EC standards. Minimum 24 hour average concentration was 66 µg/m3, which is significantly greater than the EC daily average limit (50 µg/m3). This suggests potential negative impact on human health, especially for more vulnerable groups of population, such as old age, children and people with other health problems (e.g., asthma and other respiratory diseases). Furthermore, health assessment is carried out using AirQ2.2.3 model to estimate the number of hospital admissions due to respiratory diseases. The model is based on a risk assessment approach that combines data on concentration-response functions with data on population exposure to calculate the extent of health effects expected to result from exposure to PM10. The cumulative number of estimated average hospital admission due to respiratory illnesses during the study period was 112665, cumulative number of cases per 100,000 was 2504, and the concentration-response coefficient was 2.342 (95% CI 1.899 – 2.785) per 10 ?g/m3 increase of PM10 concentration. The results are discussed in the light of investigations made in several other countries around the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".