Health parameters and their association with price in young calves sold at auction for veal operations in Québec, Canada
Bibliographic record
Abstract
The veal calf industry in Québec depends on young calves' availability at auction. Most of these calves come from dairy farms. The aim of this cross-sectional study was to determine the effect of clinical anomalies and other calf characteristics on their sale price. A total of 3,820 calves from 5 different auctions were included in this observational study. The calves were examined by a veterinarian on arrival at the auction and screened for umbilical anomalies, the presence of nasal or eye discharge, joint abnormality, diarrhea, appearance of neonatal characteristics (compatible with age less than 1 wk), and general health status mainly based on the presence of depression and dehydration. The final multivariable model included 5 different variables (calf weight, sex, breed, abnormal joints, and general health status) and the interaction between sex and general health status. The presence of abnormal joints and unhealthy characteristics was negatively associated with standardized price. Female calves and mixed breed beef calves were positively associated with standardized price. Finally, the calves' weight was associated with standardized price in a quadratic fashion. Ongoing or previous diarrhea had no effects on standardized price. This study will be helpful for both dairy and veal producers for improving the quality of calves sold to the Québec auction market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".