Application of feeding behaviour to predict morbidity of newly received calves in a commercial feedlot
Bibliographic record
Abstract
The objective of this study was to use feeding behavior of newly received steers (average initial weight 191 kg) to detect morbidity in animals in a commercial feedlot. Two separate 32 d feeding trials were conducted in Wellton, Arizona, in July and November 1996. Radio frequency technology was used to record the total time spent within 50 cm of the feedbunk (animal presence every 5.25 s times 5.25 s) in 3 h intervals from 0600 to 2400 on a daily basis for 103 and 122 male calves in trial 1 and 2, respectively. Statistical procedures based on the cumulative sums (CUSUM) of the 3 h feeding intervals were used to detect morbid animals, compared with detection of animals deemed morbid by experienced pen riders. In trial 1, the CUSUM procedure detected animal morbidity 4.5 d earlier (P < 0.001) than the feedlot personnel. In trial 2, the CUSUM procedure detected animal morbidity 3.7 d earlier (P < 0.001) than feedlot pen riders. Overall accuracy, positive predictive value and sensitivity of the CUSUM prediction method were 87, 91, and 90%, respectively. Combined trial data suggest that feeding behavior during the first 30 d cattle are in a receiving pen, as collected with radio frequency technology and analyzed with CUSUM charts, may be used to detect animal morbidity approximately 4.1 d earlier (P < 0.001) than conventional methods typically employed in commercial feedlots. Key words: Feeding behavior, morbidity, quality control, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".