Infectious agents in feral swine in Ohio, USA (2009-2015): A low but evolving risk to agriculture and public health
Bibliographic record
Abstract
As highly mobile and prolific animals, feral swine threaten public and livestock health. To quantify these threats, we analyzed disease surveillance samples to estimate the prevalence of key pathogens and parasites in feral swine captured in Ohio. Between 2009 and 2015, samples from 205 feral swine were tested for up to 13 pathogens. Methicillin susceptible Staphylococcus aureus was cultured from 29 of 138 (21%) individuals and antibodies to Leptospira spp. (16/69; 23%) , Toxoplasma gondii (11/76; 15%), Trichinella spiralis (4/69; 6%), hepatitis E virus (1/32; 3%), Brucella spp. (2/138; 1%), and influenza A virus (2/176; 1%) were also detected. Lungworms ( Metastrongylus spp . ) were frequently identified (46/55; 84%). Evidence of infection or exposure was not detected to Mycobacterium bovis , classical swine fever , pseudorabies , and porcine reproductive and respiratory syndrome. All positive Brucella and hepatitis E virus samples and 63% (10/16) of the positive Leptospira spp. samples came from individuals identified as illegal out-of-state feral swine introductions. Results indicated an overall low prevalence of pathogens in feral swine in Ohio; however, the importation of live feral swine from other states remained an important concern for pathogen introduction and spread.
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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 | 0.001 |
| 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".