Production Effects of Pathogens Causing Bovine Leukosis, Bovine Viral Diarrhea, Paratuberculosis, and Neosporosis
Bibliographic record
Abstract
The primary purpose of this research was to determine associations among seropositivity for bovine leukemia virus (BLV), bovine viral diarrhea virus (BVDV), Mycobacterium avium ssp. paratuberculosis (MAP), and Neospora caninum (NC) and each of 3 outcome variables (305-d milk, fat, and protein production) in Canadian dairy cattle. Serum samples from up to 30 randomly selected cows from 342 herds on monthly milk testing were tested for antibodies against BLV (IDEXX ELISA; IDEXX Corporation, Westbrook, ME), MAP (IDEXX or Biocor ELISA; Biocor Animal Health, Inc., Omaha, NE), and NC (IDEXX or Biovet ELISA; Biovet Inc., St. Hyacinthe, Quebec, Canada). Up to 5 unvaccinated cattle over 6 mo of age were tested for virus-neutralizing antibodies to the Singer strain of type 1 BVDV. Dairy Herd Improvement records were obtained electronically for all sampled cows. Linear mixed models with herd and cow as random variables were fit, with significant restricted maximum likelihood estimates of outcome effects being obtained, while controlling for potential confounding variables. Bovine leukemia virus seropositivity was not associated with 305-d milk, 305-d fat, or 305-d protein production. Cows in BVDV-seropositive herds (at least one unvaccinated animal with a titer > or =1:64) had reductions in 305-d milk, fat, and protein of 368, 10.2, and 9.5 kg, respectively, compared with cows in BVDV-seronegative herds. Mycobacterium avium ssp. paratuberculosis seropositivity was associated with lower 305-d milk of 212 kg in 4+-lactation cows compared with MAP-seronegative 4+-lactation cows. Neospora caninum seropositivity in primiparous cows was associated with lower 305-d milk, fat, and protein of 158, 5.5, and 3.3 kg, respectively, compared with NC-seronegative primiparous cows. There were no interactions among seropositivity for any of the pathogens and their effects on any of the outcomes examined, although the low MAP seroprevalence limited this analysis. Results from this research will contribute to understanding the economic impacts of these pathogens and justify their control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".