Establishment of a herd negative for porcine reproductive and respiratory syndrome virus (PRRSV) from PRRSV-positive sources
Bibliographic record
Abstract
Objective: To establish a herd negative for porcine reproductive and respiratory syndrome virus (PRRSV) from PRRSV-positive sources by management of the gilt pool and batching of pig flow. Methods: Two groups of PRRSV-positive gilts were housed in common acclimatization areas for 70 to 100 days, bred in an off-site finishing facility, and farrowed in a separate facility. Piglets weaned at 5 to 7 days of age were moved to off-site nurseries in weekly batches and mixed with cohort sentinel piglets from a PRRSV-negative herd. Each nursery batch was tested for PRRSV twice by PCR and once by ELISA. Two to five negative batches were grouped in off-site grower facilities, and tested again. Negative groups moved to a quarantine facility. Negative quarantine groups were used for stocking the PRRSV-negative herd. Results: Of the 31 batches of nursery pigs produced, three batches, born 2, 4, and 6 weeks after farrowings started, were PRRSV-positive. Six grower groups (comprising 23 nursery batches) that were PRRSV-negative after repeated testing were assembled into five groups in a quarantine facility and remained PRRSV-negative. Two grower groups were rejected. A total of 9500 pigs were produced, from which 3415 pigs were selected to stock the PRRSV-negative herd. Implications: A PRRSV-negative population was established from positive sources by managing the gilt pool and batching the pig flow, allowing for preservation of elite genetics. It appeared that PRRSV infection, indicated by lack of seroconversion in the offspring, eventually either disappeared or became inactive in the donor gilt population.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".