Effects of Carcass Weight Class and Postmortem Aging on Carcass Characteristics and Sensory Attributes in Grain‐Fed Veal
Bibliographic record
Abstract
ABSTRACT Thirty‐six Holstein bull calves were used to examine the effects of carcass weight and postmortem aging on carcass characteristics and sensory attributes of grain‐fed veal. Three carcass weight classes (hide‐on) included light (< 165 kg), medium (177 to 186 kg), and heavy (195 to 204 kg) with 12 animals per class. Postslaughter, longissimus (LM) and semimembranosus (SM) roasts were aged for 2, 7, and 14 d. Carcass characteristics were generally similar ( P > 0.10) across weight class. Weight class did not affect ( P > 0.10) tenderness in LM or SM or flavor in LM. Longissimus muscle from light weight carcasses were juicier ( P < 0.07) than heavier carcasses while flavor was lower in SM for medium compared with heavy carcasses. Aging tended to increase ( P < 0.100) tenderness attributes for LM and SM. Veal flavor increased ( P =.014) with 7 d aging for LM. Postmortem aging should be considered for routine processing of veal to improve meat 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 | 0.000 | 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".