Comparison of feeding behavior between black and red Angus feeder heifers
Bibliographic record
Abstract
The objective of this study was to compare feeding behavior between red and black Angus heifers during a 161-d finishing period as a potential explanation for performance differences. Sixty-eight single-sourced purebred red (n = 35) and black (n = 33) Angus heifers, leptin genotype TT, and average starting weight 360 kg (±19 kg) were used. Heifers were randomly and equally allocated into one of two feedlot pens, equipped with five feed bunks that recorded feeding behavior. Individual time spent at the feed bunk, interval between feeding events, feed intake, and meal frequency were recorded daily, and eating rate was calculated. Heifers were fed a barley-based diet (>75% concentrate). After 161 d, at the end of the feeding period, feedlot performance was calculated as average daily gain (ADG) and gain to feed conversion rate. Additionally, carcass data were obtained from the abattoir. Overall, black Angus heifers ate more, spent more time at the feed bunk, and had more meals compared with red Angus (P < 0.001). Red Angus heifers had better gain to feed ratios (P < 0.02) and significantly more red heifers were assigned to Canadian yield category 1 (≥59% lean meat) compared with black heifers (P = 0.02), whereas black heifers had higher back fat thickness throughout the study (P ≤ 0.04). All other performance parameters (ADG and carcass weight) were not different.
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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 | 0.000 | 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".