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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".