Ultrasonic imaging of marbling at feedlot entry as a predictor of carcass quality grade
Bibliographic record
Abstract
This study evaluated the ability of ultrasonic examination at entry into the feedlot to predict carcass traits. Feeder calves (487) from eight Prince Edward Island feedlots were examined with an Aloka 500 ultrasound and Critical Vision® image analysis software to determine carcass attributes (backfat, ribeye area and intramuscular fat) at feedlot entry. These measures, along with potential confounders, were evaluated for their ability to predict carcass grade. Three statistical procedures (multinomial logistic regression, constrained multinomial logistic regression and a proportional odds logistic regression) were used to evaluate the data. After evaluation, final analyses were performed using the constrained multinomial logistic regression (adjacent category) procedure. All three ultrasound determined carcass attributes were significantly associated with slaughter grade. The odds of being one grade category higher (e.g., AAA) versus the adjacent category (e.g., AA) were 1.74, 1.37 and 0.98 per percentage point intramuscular fat, mm of backfat or cm2 of ribeye area, respectively. Heifers were 2.1 times more likely to be in the next higher grade category than steers. Feedlot of origin, days on feed and carcass weight were also significant predictors of final grade. Key words: Cattle, beef; carcass traits; ultrasound; marbling; carcass grade
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".