Evaluation of sensory attributes of Hanwoo <i>Longissimus dorsi</i> muscle and its relationships with intramuscular fat, shear force, and environmental factors
Bibliographic record
Abstract
We examined the relationship of Hanwoo (Bos taurus coreana) beef sensory attributes with intramuscular fat (IMF), Warner–Bratzler shear force (WBSF), and other environmental factors. Samples of 458 beef carcasses including 32 cows, 34 steers, and 392 bulls were analyzed for IMF percentage, WBSF, and important sensory attributes (tenderness, juiciness, and flavour-likeness). Results revealed that steer beef had significantly higher scores for all three sensory attributes than beef from cows and bulls. While juiciness and flavour-likeness differed (p < 0.05) among bulls and cows, they were not associated an effect on tenderness. All sensory attributes and WBSF were correlated (p < 0.001) with IMF; the highest correlation coefficient was for tenderness (0.55) while a negative coefficient (−0.39) was found for WBSF. The influence of IMF level on all three sensory attributes of bull beef significantly increased with IMF level. All sensory attributes of steer and bull beef showed negative relationship (p < 0.01) with WBSF, where highest correlation coefficient (−0.8) found for steer beef tenderness. Furthermore, tenderness and flavour-likeness showed lower ranks for meats slaughtered during the winter season; however, juiciness did not differ (p < 0.05) among seasons.
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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".