Microbiological identification and analysis of swine tonsils collected from carcasses at slaughter.
Bibliographic record
Abstract
The primary objective of this 7-month study was to determine the prevalence of porcine pathogens of the tonsil of the soft palate of swine at slaughter. Additional objectives were to determine if sampling the carcasses of normal or abnormal hogs provided different microbiological profiles and if the slaughter plant provides a feasible sampling frame and environment for detecting and monitoring important pathogens in tonsils that have health implications for both swine and humans. A total of 395 samples were collected from 264 farms. Of these, 180 tonsils were collected from normal carcasses and 215 tonsils were collected from carcasses that were diverted to the hold rail. Laboratory testing included bacteriological culture and identification as well as real time-polymerase chain reaction (PCR) testing for porcine reproductive and respiratory syndrome virus (PPRSV) and immunohistochemistry (IHC) for porcine circovirus-2 (PCV-2). The most commonly isolated bacteria included: Streptococcus suis (53.7%), Arcanobacterium pyogenes (29.9%), Pasteurella multocida (27.3%), and Streptococcus porcinus (19.5%). Virus screening revealed evidence of PRRSV and PCV-2 in 22.0% and 11.9% of the samples, respectively. Salmonella Typhimurium and Yersinia enterocolitica were isolated in 0.5% and 1.8% of the samples, respectively. Tonsils collected from the hold rail were more likely to be positive for Staphylococcus hyicus [odds ratio (OR) = 7.51, confidence interval (CI) = 2.89 to 19.54], Streptococcus porcinus (OR = 9.93, CI = 4.27 to 23.10), and Streptococcus suis (OR = 2.16, CI = 1.45 to 3.24). Tonsils collected from abnormal carcasses were less likely to be positive for Staphylococcus aureus (OR = 0.05, CI = 0.005 to 0.482).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".