Bibliographic record
Abstract
Study objectives. The objectives of this study were twofold. First, we used simulation to investigate three statistical methodologies for analyzing recurrent event data in the presence of environmental covariates to determine which procedure performs well under what situation. Secondly, we investigated the association between daily air pollution and hospital admissions of respiratory diseases by analyzing data from Vancouver, British Columbia. Settings and study population. Five air pollutants: carbon monoxide (CO), coefficient of haze (CoH), nitrogen dioxide (NO 2), sulfur dioxide (SO2) and particulate matter 10 microns or less in diameter (PM10) were considered in this study. The event of interest was daily respiratory hospital admissions of residents (65+ years) of Vancouver, B.C. from April 01, 1995 to March 31, 1999. The dates of hospital admission for each individual's repeated visits due to respiratory diseases were recorded. Statistical methodologies. Three statistical methods were studied in this study, namely, Dewanji and Moolgavker's model (2000, 2002) based on a Poisson process assumption (Model I), Nividi's model (1998, 2002) for bidirectional case-crossover designs (Model II), and the usual time series analysis using a generalized linear model with natural splines (ns) to smooth time (Model III). A simulation method was used to evaluate and compare these procedures. The mean square error (MSE) was the criterion used for evaluation. (Abstract shortened by UMI.)Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .K45. Source: Masters Abstracts International, Volume: 44-01, page: 0379. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".