The impact of commingling preconditioned calves on mortality, morbidity and performance in a feedlot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it