Effects of forage finishing methods with alfalfa on cattle growth performance and beef carcass characteristics, eating quality, and nutrient composition
Bibliographic record
Abstract
Over 2 yr, yearling steers (n = 200) were used to evaluate growth performance, carcass, meat quality, and nutrient composition traits as affected by management regimen comparing three methods of forage finishing (alfalfa pasture, hay, and silage) versus a high corn diet. Management regimen × year interactions (P < 0.01) for average daily gain, dry matter intake, gain-to-feed, carcass weight, and grade fat were due to lower performance for hay-fed cattle in years 1 vs. 2. Carcass, meat quality, and taste panel traits were generally similar (P > 0.10) across method of forage finishing. Trained taste panels found longissimus muscle from grain-fed beef to be more (P < 0.01) tender, juicy, and flavourful than forage finished beef, with lower (P ≤ 0.05) ratings for tenderness and juiciness for hay- vs. silage-finished beef. Corn finished beef contained greater amounts of oleic and monounsaturated fatty acids and lower amounts of omega-3 and polyunsaturated fatty acids than forage finished beef (P ≤ 0.04). Although the method of forage finishing may not affect most performance, carcass, and meat quality (pH, colour, intramuscular fat content, and shear force) traits, there may be concerns with tenderness and juiciness for beef from cattle finished on alfalfa hay.
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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) | 0.001 | 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 | 0.000 |
| 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".