The impact of commingling preconditioned calves on mortality, morbidity and performance in a feedlot
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is the most important disease in the North American beef industry, causing substantial economic losses due to morbidity and mortality, including treatments, reduced performance, and increased antimicrobial use. Preconditioning (PC) to mitigate BRD was proposed as early as 1967 and constitutes management practices that reduce stressors and optimize resilience through vaccination against bacterial and viral pathogens, optimized timing of dehorning, castration, best weaning strategy, and training calves to eat from a bunk and drink from a water source at least 45 d before transport to the feedlot. Despite proven profits for preconditioning of beef calves, PC hasn’t been established in the current beef industry. Besides the lack of premiums paid, there is also the question if commingling of PC and auction-derived (AD) calves in the feedlot can hamper PC calves’ expected growth and health advantages. Therefore, our objective was to evaluate the impact of optimally preconditioned calves on mortality, morbidity and average daily gain (ADG) during the first 40 days in the feedlot when PC calves where commingled with different proportions of AD calves (25, 50, 75%).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".