Can potential for dark cutting be predicted by phenotype? Relationship between sex, carcass characteristics, and the incidence of dark cutting beef
Bibliographic record
Abstract
Recent research indicated that reduced carcass muscling and weight are associated with beef dark cutting. Existing data from a single farm (44 heifers, 136 steers) from three normal beef quality grades (Canada AAA, AA, A) and the dark cutting grade (Canada B4) (n = 35, 106, 28, and 11, respectively) were used to identify relationships between sex, live animal, and carcass characteristics and the incidence of dark cutting. Categorical modelling showed a trend (P = 0.106) for heifer carcasses to have a greater probability [11.36 ± 4.78% standard error of the mean (SEM)] of dark cutting than carcasses from steers (4.4 ± 1.76% SEM). Mixed model analysis of variance indicated dark cutting heifers weighed less than cattle from normal grades at weaning (P = 0.0229) and slaughter (P = 0.0295). Logistic regression revealed that the probability of each carcass grade occurring was influenced by dry matter intake (DMI) (P = 0.0034), and the probability of dark cutting was lowest (P = 0.0286) in cattle with carcasses greater than 300 kg. Results suggested that cattle at risk of dark cutting may be identified by weight, average daily gain (ADG), and feed intake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".