RELATIONSHIP BETWEEN CATTLE SEX, PRODUCTION AND CARCASS CHARACTERISTICS AND THE INCIDENCE OF DARK CUTTING BEEF
Bibliographic record
Abstract
Carcasses of young cattle having > 2 mm subcutaneous fat and bright red rib eye muscles (m. longissimus thoracis) are graded Canada A, AA, AAA or Prime according to intramuscular fat content. Carcasses from young cattle with dark red or purple coloured rib eye muscle are downgraded to Canada B4, the dark cutting grade. Dark cutting is caused by muscles not having sufficient glycogen to fuel post mortem anaerobic glycolysis and reduce muscle pH below 6. The value of dark cutting carcasses is reduced by up to $1 per kg because muscle colour is unattractive to consumers and prone to microbial growth. Because of the substantial economic penalty, predicting and preventing dark cutting would be financially advantageous for beef producers and abattoirs. This study tested the hypothesis that the likelihood of a beef animal producing a Canada B4 carcass can be predicted using animal sex, growth performance, body weight, muscle size and carcass characteristics. Materials and Methods An existing data set containing dry matter intake (DMI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), ultrasound rib eye area (uREA), ultrasound subcutaneous fat depth (uSFD), ultrasound marbling score (uMS), carcass weight (CarWt), grade fat depth (gFD), grade rib eye area (gREA) and grade marbling score (gMS) collected between 2003 to 2011 from 845 steers and heifers from three different farms was used test the effect of gender on dark cutting. A sub-set of cattle that graded Canada AAA (n =28), AA (n =29), and A (n =15) was also drawn from this data set to relate carcass and animal phenotypic characteristics to the probability of dark cutting or Canada B4. Canada A, AA and AAA cattle selected were matched by lot and date of birth to Canada B4 animals (n =
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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) | 0.001 | 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".