Economic impact of digital dermatitis, foot rot, and bovine respiratory disease in feedlot cattle
Bibliographic record
Abstract
Abstract Digital dermatitis (DD) has emerged in North American feedlots, although production and economic impacts are not fully understood. Objectives of this study were to: (1) estimate the economic impact of a single case of DD, foot rot (FR), and bovine respiratory disease (BRD) in feedlot cattle and (2) determine its impact on average daily gain (ADG). Feedlot cattle health and production records were available from two feedlots for a 3-yr interval. The dataset consisted of 77,115 animal records, with 19.3% (14,900) diagnosed with a disease. Diseased animals were categorized into five groups: DD, FR, BRD, other diseases (OT), and two or more diseases (TM), with a treatment cumulative incidence of 6.0%, 59.1%, 10.7%, 12.7%, and 11.5%, respectively. FR was the disease with the highest cumulative incidence in both heifers and steers (58.8% and 59.6%, respectively). Of all fall-placed cattle diagnosed with any disease, 48.1% of the cases were FR. DD affected the partial budget in five out of the eight groups of cattle, with the highest impact of DD seen in grass yearling heifers and grass yearling steers: $-98 and $-96 CAD, respectively, relative to their healthier counterparts. Healthy cattle had a significantly higher ADG when compared with DD cattle in five of the eight categories, ranging from 0.11 kg/d in winter-placed heifers to 0.17 kg/d in fall-placed steers. In the economic analysis, it was concluded that on an individual animal basis, BRD was the most impactful of all analyzed diseases, whereas DD was second, marking the importance of controlling and mitigating this foot condition. Identifying differential effects of diseases on a partial budget analysis and ADG of the types of cattle stratified by sex enables feedlot producers to focus control and mitigation strategies on specific groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".