Calving management practices on Canadian dairy farms: Prevalence of practices
Bibliographic record
Abstract
Little information is available about current practices around calving in dairy cattle. The aim of this study was to describe calving management practices in the Canadian dairy industry related to housing, calving protocols, monitoring of parturition, and calving assistance. Information was gathered by in-person interviews from 236 dairy farms from 3 Canadian provinces (Alberta, Ontario, and Québec) with freestalls and an automatic milking system (n=24), freestalls with a parlor (n=112), and tiestalls (n=100). The most commonly used types of calving facilities were group calving pens (35%) followed by individual calving pens (30%). Tiestalls were used by 26% of all surveyed producers as their main type of calving area (49% of the tiestall, 7% of the freestall with parlor, and 13% of the automatic milking system farms). Written protocols related to calving were found on only 7% of the farms visited, and only 50% of those protocols were developed with a veterinarian. However, 90% of producers kept written records of calving difficulty. Monitoring of cows around calving occurred 5 times more often during the daytime (between morning and evening milking) compared with nighttime. Cameras were used to monitor cows around and during calvings on 18% of farms. Sixteen percent of producers vaginally palpated all animals during calving. Twenty-seven percent of producers interviewed assisted all calvings on their farms by pulling the calf, and 37% assisted all heifers at calving. According to the producers' reported perception, 93% of them had "a minor problem" or "no problem" with calving difficulties on their farms. This study provides basic data on current calving practices and identifies areas for improvement and potential targets for knowledge transfer efforts or research to clarify best management practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".