Effects of production system and slaughter weight endpoint on growth performance, carcass traits, and beef quality from conventionally and naturally produced beef cattle
Bibliographic record
Abstract
Glanc, D. L., Campbell, C. P., Cranfield, J., Swanson, K. C. and Mandell, I. B. 2015. Effects of production system and slaughter weight endpoint on growth performance, carcass traits, and beef quality from conventionally and naturally produced beef cattle. Can. J. Anim. Sci. 95: 37–47. Effects of production system and slaughter endpoint on performance, carcass traits, and beef quality were investigated in 64 Simmental cross steers (minimum 75% Continental breeding). Cattle were allocated to: (1) conventional production system based on use of implants and dietary ionophores or (2) natural production system in which no implants or ionophores were used. Within each production system, cattle were allocated for slaughter at 545 or 636 kg liveweight. Steers were fed an 85.5% concentrate diet based on high-moisture corn, soybean meal, and alfalfa silage. Average daily gain tended to be greater (P<0.06) in conventional production system cattle, while there was a trend (P<0.08) for production system by endpoint interactions for dry matter intake and gain to feed. Natural production system cattle tended to have greater (P<0.08) marbling and percent intramuscular fat (%IMF) with lower (P<0.09) longissimus shear force, while production system by endpoint interactions were present (P ≤ 0.03) for%IMF and carcass lean composition via rib dissection. At-home consumer evaluation of longissimus muscle steaks found tenderness, juiciness, flavour, and overall acceptability rankings were greater (P<0.01) for steaks slaughtered from heavier cattle (636 vs. 545 kg liveweight). Marketing cattle at lighter slaughter weights may have benefits for performance at the expense of eating quality.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".