An Investigation into Dairy Cow Welfare in Canada: A Cow and a Human Perspective
Bibliographic record
Abstract
This thesis is an investigation into better understanding dairy cow welfare in Canada. A national cross-section study was undertaken to assess and provide producers feedback on dairy cattle housing, management and care in Canada. This included the assessment of hock and knee injuries and their risk factors for cows housed in tiestall systems. On average, 56% of cows within a herd were discovered to have hock injuries, and 43% of cows within a herd were discovered to have knee injuries. Factors associated with greater odds of hock and knee injuries included factors such as stall dimensions, stall surface, BCS, DIM and lying time. One year following this project, phone interviews were undertaken with these producers. Simultaneously, a separate survey was undertaken with dairy experts to assess the difficulty of making changes to improve cow welfare on farm. It was discovered that a majority (72%) of producers implemented some sort of change related to improving animal welfare following the intervention, however 17% of producers implemented changes that were not related to the weaknesses identified on their farm. The most common barriers identified to implementing changes to improve dairy cow welfare were lack of time and lack of fund. Dairy experts identified stall design changes as the most difficult to improve dairy cow welfare. Lastly, a Delphi survey was completed by dairy experts in Canada to better understand how the dairy industry defines and measures dairy cow welfare and seek consensus on a set of gold standard animal-based targets for realistically optimal dairy cow welfare. The study resulted in consensus within dairy stakeholders on how to define and measure dairy cow welfare. Most (72%) responded that they include a combination of natural living, health, affective state and production factors in their definition of dairy cow welfare, and all stated they would use animal-based measures, often in combination with other measures to assess dairy cow welfare. Lameness was the most frequently mentioned animal-based measure to assess dairy cow welfare. The survey participants were able to come to a consensus on 16 of 21 animal-based targets to describe a herd with realistically optimal welfare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".