An investigation of time series and case-crossover analyses of air pollution and asthma hospital admission data for children in Toronto (Ontario).
Bibliographic record
Abstract
Air pollution has been a popular topic of study over the years. It causes great harm to our environment (global warming) not to mention our health (cancer, heart disease, respiratory disease, etc.). Many people have investigated the damaging relationship between air pollution and mortality and morbidity, using different methods along the way. The different methods yield results that are not directly comparable with one another because the methods use different strategies. Air pollution data and hospital admissions data for asthma patients aged six to twelve in the Toronto area from January 1, 1981 to December 31, 1993 were gathered and analyzed under a variety of time series and case-crossover designs. The lack of consistency in the results among the techniques led us to perform a simulation in order to choose the most accurate method to analyze this Toronto data. While the time series approach produced fairly accurate results, the bidirectional case-crossover using the exact method of approximation was the overall best technique of analysis.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L589. Source: Masters Abstracts International, Volume: 41-04, page: 1098. Adviser: Karen Fung. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".