Canadian beef quality audit 1998-99.
Bibliographic record
Abstract
The second beef quality audit was conducted in Canada in 1998-99 to determine the prevalence of quality defects in slaughtered cattle and to monitor changes since the first audit in 1995. Approximately 0.6% of the number of cattle processed annually in Canada were evaluated. Brands were observed on 49% and tag was observed on 43% of the hides. Both brands and tag had increased from 1995. Seventy percent of the cattle were polled and 5% had full horns; thus, the number of horned cattle had decreased from 1995. Bruises were found on 54% of the carcasses, which was a decrease from 78% in 1995. Sixty-eight percent of the bruises were minor, 28% major, and 4% critical in severity. The distribution of bruises on the carcass was 17% on the chuck, 36% on the rib, 30% on the loin, and 16% on the round. Grubs were observed on 0.008% of the carcasses, and surface injection site lesions were observed on 0.2% of the whole carcasses, a decrease from the 1.3% seen in 1995. Seventy-two percent of the livers were passed for human food and 14% for pet food; 14% were condemned. Approximately 64% of the liver losses were due to abscesses. Five percent of the heads and tongues and 0.3% of the whole carcasses were condemned. The hot carcass weight was highly variable in all cattle, averaging 353 kg (s = 43). The average ribeye area was 90 cm2 (s = 13). Both hot carcass weight and ribeye area had increased from 1995. The average grade fat was 9 mm (s = 5), ranging from 0 mm to 48 mm. Lean meat yield averaged 58.8% (s = 4.6). One percent of the carcasses were devoid of marbling, 17% were Canada A, 49% were Canada AA, 32% were Canada AAA, and 1% were Canada Prime, which was an increase in marbling from 1995. Dark cutters were found in 1% of all carcasses; 1% of steers, 0.5% of heifers, 3% of cows, and 14% of bulls. Three percent of the carcasses were underfinished and 13% were overfinished. The number of overfinished carcasses had increased from 1995. Stages, steers with bullish traits, were infrequently observed in 0.5% of the steers, and 0.2% of the steers and 0.3% of the heifers had poor conformation. Yellow fat was not observed in any steers or heifers, but it was found on 65% of the cow carcasses. Only 0.6% of the heifers had an aged carcass, based on skeletal maturity. Based on August 1998 to July 1999 prices, it was estimated that the Canadian beef industry lost $82.62 per head processed, or $274 million annually, from quality nonconformities, which was an increase from 1995. Additional improvements in management, feeding, handling, genetics, marketing, and grading are needed in the beef industry to reduce quality defects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".