Hot topic: Health and welfare challenges in the marketing of male dairy calves—Findings and consensus of an expert consultation
Bibliographic record
Abstract
A diverse group of Canadian experts was convened for a focused 2-d discussion on potential health and welfare problems associated with the marketing (i.e., transportation and sale) of male dairy calves. Written notes and audio recording were used to summarize the information provided on transport times and marketing practices. Content analysis was used to develop a consensus statement on concerns, possible solutions, and recommendations to improve male dairy calf marketing. The group noted that calves across all Canadian regions are commonly transported at 3 to 7 d of age and undergo transport for 12 to 24 h or longer depending on the location of their dairy farm of origin. Calves in some regions are marketed almost exclusively through auction markets, whereas others have more direct sales. A need was identified for better criteria for calf fitness for transport, maintaining farm biosecurity, reducing the use of antimicrobial therapy in calf production, and improving education for farmers and veterinarians on the importance of neonatal care for male dairy calves before transportation. Experts noted that major changes in male dairy calf marketing will be required to comply with amendments to the federal Health of Animals Regulations (Part XII) on animal transportation; collaborative effort will be needed to safeguard animal health and welfare as this transition is made.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".