Improved beef tenderness using a modified on-line carcass suspension method with, or without low voltage electrical stimulation
Bibliographic record
Abstract
Carcasses from 59 market-ready steers of an estimated Canada 1 yield grade were used to compare the effects on carcass grade and meat quality of modified, on-line, altered suspension (MOLAS), with or without the application of low voltage electrical stimulation (LVES). Due to conformational changes at the grade site, MOLAS negatively affected both yield and quality grades. However, MOLAS did significantly (P < 0.05) reduce shear force in the longissimus thoracis (LT) and longissimus lumborum (LL) muscles (decreases of 0.65 and 2.11 kg, respectively). In the LT, MOLAS and LVES had similar, non-additive effects; a similar proportion of carcass-es (86%) were classified as tender (shears <5.6 kg) compared to controls (CON; 60%). In the LL, MOLAS was more effective than LVES, and combined MOLAS and LVES had the greatest effect. Only 23.3% of CON carcasses were categorized as tender compared to 53.3% for MOLAS, 27.6% for LVES and 89.7% for combined LVES and MOLAS. Consumers were able to discern these differences, rating MOLAS steaks more highly than CON steaks for almost all hedonic and descriptive traits. Despite significant improvements to tenderness the MOLAS procedure had a negative influence on carcass grade due to conformational changes. The present grading system would need to be modified to accurately assess MOLAS carcasses. Key words: Altered suspension, beef tenderness, low voltage electrical stimulation
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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) | 0.002 | 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".