Machine effects on accuracy of ultrasonic prediction of backfat and ribeye area in beef bulls, steers and heifers
Bibliographic record
Abstract
Pre-slaughter ultrasound and carcass measurements of ribeye area (REA) and backfat (FAT) were recorded on composite beef bulls (n = 60), heifers (n = 60) and steers (n = 60). Breed composition of the composite was: 0.44 British (Hereford, Angus and Shorthorn) 0.25 Charolais, 0.25 Simmental and 0.06 Limousin. The Aloka SSD-1100 (AL) and the Tokyo Keiki CS 3000 (TK) ultrasound machines were compared by evaluating the difference between ultrasound and carcass measurements (bias), and the standard error of prediction (SEP). AL under-predicted REA in all three sexes while TK overpredicted heifers and steers and underpredicted bulls. Both machines were similar in accuracy among bulls for REA. For FAT AL underpredicted all three sexes while TK underpredicted heifers and had very small bias for bulls and steers. SEP for FAT were similar for both machines. Both machines underpredicted REA in larger muscled cattle and overpredicted in smaller-muscled cattle. Both machines also underpredicted FAT in fatter animals and overpredicted FAT in leaner animals. Machines were similar in accuracy among cattle with larger REA but differed significantly (P < 0.05) among smaller-muscled cattle. Machines were comparable in accuracy among animals of all FAT sizes. This study demonstrates that there is an important relationship between machine and the size and depth of muscle and backfat, respectively, and consequently between machine and sex, in accuracy of ultrasound prediction. Key words: Beef cattle, ultrasound, accuracy, back fat, ribeye area
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".