A retrospective study using condemnation data of market hogs in Ontario for quantitative disease surveillance
Bibliographic record
Abstract
A longitudinal study was conducted to investigate whether Ontario provincial abattoirs are a useful source of information for syndromic disease surveillance of finisher hogs. Multivariable negative binomial models were applied to examine relationships among non-disease factors and whole and partial carcass condemnations. Scan statistics were employed to detect clustering of whole carcass and partial carcass condemnations related to lungs and kidneys. Associations between higher condemnation rates and larger abattoir processing capacity were consistent for all models, while interactions between region and year demonstrated different trends between the models. Whole hog carcass condemnation data performed better than partial carcass condemnation data at detecting high rate clusters consistent with a documented porcine circovirus-associated disease outbreak. Improvements in the reporting of partial carcass condemnation data and consideration of non-disease factors such as abattoir size and region are required if these data are incorporated into a syndromic surveillance system for Ontario swine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".