Hog Mandibular Lymph Node Abnormalities and Bacteriological Contamination at Slaughter in Canada
Bibliographic record
Abstract
In the context of meat inspection modernization, the current mandatory incision and visual inspection of all hog mandibular lymph nodes (MLNs) to detect signs of infection by Mycobacterium bovis, a zoonotic infection is examined. Canadian data of MLN pathology and microbial contamination are scarce and the performance of the current regulatory inspection of MLNs in actually detecting lesions and contamination by Mycobacterium bovis has not been documented. A survey of MLN condition in market hogs according to various inspection schemes was undertaken to fill the data gap. One MLN of a representative sample of 149 483 hogs were submitted to standard pathological and bacteriological analyses. Actual abnormal MLNs actually were rare (below 8%). They are often under detected by the current inspection because of limited sensitivity (18%). Such abnormalities, even undetected, have very limited, if any, impact on human health since the pathological and microbiological analyses failed to provide evidence of Mycobacterium bovis infection. On the other hand, MLNs can be contaminated with several bacteria potentially pathogenic to humans, raising the risk of cross-contamination during the inspection with incision. Finally, the current incision and visual inspection of all hog MLNs and the proposed visual-only inspection perform similarly in identifying abnormal MLNs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".