Comparison of alternative beef production systems based on forage finishing or grain-forage diets with or without growth promotants: 2. Meat quality, fatty acid composition, and overall palatability
Bibliographic record
Abstract
Five beef cattle management regimens were evaluated for their effect on meat quality, fatty acid composition, and overall palatability of the longis-simus dorsi (LD) muscle in Angus cross steers. A 98-d growing phase was conducted using grass silage with or without supplementation of growth promotants (Revalor G and Rumensin) or soybean meal. Dietary treatments in the finishing phase were developed with or without supplementation of growth promotants based on exclusive feeding of forages with no grain supplementation, or the feeding of grain:forage (70:30) diets. Growth promotants increased (P < 0.01) shear force and tended (P = 0.06) to increase toughness of the LD muscle due to limited postmortem proteolytic activity (lower myofibrillar fragmentation index value; P = 0.02). Grain feeding increased DM and intramuscular fat content (P = 0.03 and P = 0.05, respectively) in the LD but decreased the sensory panel tenderness score (P = 0.01). Growth promotants increased (P </= 0.05) the proportion of C18:0, C20:0, trans isomers of C18:1, and cis-9, trans-11 C18:2. Exclusive feeding of forages increased the proportion of cis-9, cis-12, cis-15 C18:3 as well as several other isomers of the n-3 family and decreased in the ratio of n-6 to n-3 fatty acids in the LD muscle as compared with supplementing grain (P < 0.05). In addition, the forage-based diet increased (P < 0.01) the concentration in the intramuscular fat of several intermediates (cis-9, trans-11, cis-15 C18:3; trans-11, cis-15 C18:2; trans-11 C18:1) of ruminal biohydrogenation. Forage feeding also increased the proportion of cis-9, trans-11 C18:2 (P < 0.01) and decreased the concentration of trans-10 C18:1 in the LD muscle (P = 0.03). It is concluded that quality demands of health-conscious consumers can be met through a forage-finishing and growth promotants-free beef production system.
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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".