Weather and livestock risk factors for <i>Escherichia coli</i> O157 human infection in Alberta, Canada
Bibliographic record
Abstract
This study investigated the extent to which proximity to cattle and weather events in Alberta predispose human populations to E. coli O157 disease. Cases of human E. coli O157 infection in Alberta between 2004 and 2011 were obtained from the province's Communicable Disease Reporting System and Discharge Abstract Database. Regression models based on spatial area incorporated human infection data with livestock and weather covariates. A variety of regression models were applied (i.e. least squares, spatial lag/error, Poisson, negative binomial) to test the most appropriate approach. Ratios for the total number of calves, bulls and beef cows to human population were highlighted as significant cattle density variables in all final best-fitting models. Weather variables were not significant in final regression models averaged over the full study period. Our results provide evidence of a significant association between measures of cattle density and human E. coli O157 disease in Alberta.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".