Diseases and Pathogens Associated with Mortality in Ontario Beef Feedlots
Bibliographic record
Abstract
This study determined the prevalence of diseases and pathogens associated with mortality or severe morbidity in 72 Ontario beef feedlots in calves that died or were euthanized within 60 days after arrival. Routine pathologic and microbiologic investigations, as well as immunohistochemical staining for detection of bovine viral diarrhea virus (BVDV) antigen, were performed on 99 calves that died or were euthanized within 60 days after arrival. Major disease conditions identified included fibrinosuppurative bronchopneumonia (49%), caseonecrotic bronchopneumonia or arthritis (or both) caused by Mycoplasma bovis (36%), viral respiratory disease (19%), BVDV-related diseases (21%), Histophilus somni myocarditis (8%), ruminal bloat (2%), and miscellaneous diseases (8%). Viral infections identified were BVDV (35%), bovine respiratory syncytial virus (9%), bovine herpesvirus-1 (6%), parainfluenza-3 virus (3%), and bovine coronavirus (2%). Bacteria isolated from the lungs included M. bovis (82%), Mycoplasma arginini (72%), Ureaplasma diversum (25%), Mannheimia haemolytica (27%), Pasteurella multocida (19%), H. somni (14%), and Arcanobacterium pyogenes (19%). Pneumonia was the most frequent cause of mortality of beef calves during the first 2 months after arrival in feedlots, representing 69% of total deaths. The prevalence of caseonecrotic bronchopneumonia caused by M. bovis was similar to that of fibrinosuppurative bronchopneumonia, and together, these diseases were the most common causes of pneumonia and death. M. bovis pneumonia and polyarthritis has emerged as an important cause of mortality in Ontario beef feedlots.
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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 | 0.000 | 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 | 0.001 | 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".