Effect of nutritional regimen including limit feeding and breed on growth performance, carcass characteristics and meat quality in beef cattle
Bibliographic record
Abstract
McGregor, E M., Campbell, C. P., Miller, S. P., Purslow, P. P. and Mandell, I. B. 2012. Effect of nutritional regimen including limit feeding and breed on growth performance, carcass characteristics and meat quality in beef cattle. Can. J. Anim. Sci. 92: 327–341. The effects of nutritional management regimen and breed on growth performance, carcass attributes, and meat quality were evaluated in 68 British (BRIT) and Continental (CONT) crossbred steers, managed on one of three nutritional regimens: (1) ad libitum access to a 77% corn diet (ALGRAIN), (2) limit feeding initially of the 77% corn diet, followed by ad libitum access to the 77% corn diet (LFGRAIN), and (3) limit feeding initially of a 90% haylage diet, followed by ad libitum access to the 77% corn diet (LFHAYL). Nutritional regimen by breed interactions (P<0.02) were present for average daily gain, dry matter intake (DMI), gain to feed, and shear force. LFHAYL regimen decreased gains to a greater extent for BRIT steers than CONT steers managed on ALGRAIN or LFGRAIN. Feed intakes were similar across nutritional regimens for CONT steers, while DMI was depressed in BRIT on LFHAYL as compared with steers started on ALGRAIN or LFGRAIN. Gain to feed was greater in BRIT cattle on LFGRAIN vs. ALGRAIN, while gain to feed was similar in CONT started on grain (ALGRAIN, LFGRAIN). Shear force was lower for beef from LFHAYL BRIT vs. beef from BRIT on ALGRAIN or LFGRAIN, while the converse was true for CONT. Limit feeding of a high-energy diet may be appropriate for cattle of British background to reduce feed input costs, while ensuring tenderness.
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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 | 0.001 |
| 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".