Wild boars as sources for infectious diseases in livestock and humans
Bibliographic record
Abstract
Wild boars (Sus scrofa) are indigenous in many countries in the world. These free-living swine are known reservoirs for a number of viruses, bacteria and parasites that are transmissible to domestic animals and humans. Changes of human habitation to suburban areas, increased use of lands for agricultural purposes, increased hunting activities and consumption of wild boar meat have increased the chances of exposure of wild boars to domestic animals and humans. Wild boars can act as reservoirs for many important infectious diseases in domestic animals, such as classical swine fever, brucellosis and trichinellosis, and in humans, diseases such as hepatitis E, tuberculosis, leptospirosis and trichinellosis. For examples, wild boars are reservoirs for hepatitis E virus, and cluster cases of hepatitis E have been reported in Japan of humans who consumed wild boar meat. In Canada, an outbreak of trichinellosis was linked to the consumption of wild boar meat. The incidence of tuberculosis owing to Mycobacterium bovis has increased in wild boars, thus posing a potential concern for infections in livestock and humans. It has also been documented that six hunters contracted Brucella suis infections from wild swine in Florida. This article discusses the prevalence and risk of infectious agents in wild boars and their potential transmission to livestock and humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".