Effect of intended purpose (dairy/beef production) on beef quality
Bibliographic record
Abstract
Lizaso, G., Beriain, Ma. J., Horcada, A., Chasco, J. and Purroy, A. 2011. Effect of intended purpose (dairy/beef production) on beef quality. Can. J. Anim. Sci. 91: 97–102. Fifteen Pirenaica (beef) heifers and fifteen Holstein-Friesian (dairy) heifers were compared to consider the effect of intended purpose of the breed on meat quality. Animals were slaughtered at around 12.4 mo of age. Carcass weight was 247.6 kg for Pirenaica and 229.2 kg for Holstein-Friesian heifers. pH24hours, moisture, fat, protein, ash, water holding capacity, CIE L*a*b* and C* and H* values, collagen content, and shear force were determined in longissimus dorsi muscle. Sensory analysis of meat was performed to obtain the texture profile, scored by a trained taste panel. Sensorial differences of meat were mainly in relation to higher fat content observed in Holstein-Friesian (2.87%) vs. Pirenaica breed (1.82%) (P<0.01). Total and soluble collagen contents for the two breeds differed (P<0.01 and P<0.05, respectively) but did not result in different meat tenderness scores by taste panelists. Higher water-holding capacity in meat of Holstein-Friesian was observed (P<0.01). CIE L*a*b*, coordinates C* and H* values were not different between breeds. Sustained juiciness and oiliness were higher in Holstein-Friesian than Pirenaica (P<0.05 and P<0.01, respectively). Results suggest that beef characteristics of young heifers of dairy and beef breeds are similar with the exception of characteristics related to juiciness.
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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 | 0.002 |
| 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) | 0.003 | 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".