Factors influencing how Canadian dairy producers respond to a downer cow scenario
Bibliographic record
Abstract
Understanding how downer cattle are managed allows for the evaluation of strengths and weaknesses in these practices, which is an important step toward improving the care these animals receive. The objective of this cross-sectional study was to analyze factors associated with the care and management of downer cattle by Canadian dairy producers. Data were obtained from the 2015 National Dairy Study, and analysis was limited to the 371 respondents completing the downer cow scenario. The scenario described a downer cow that the producer wanted to keep in their herd but must be moved, and was followed by questions addressing the cow's care and management. Using multivariable logistic regression models, associations between respondent demographics and farm characteristics, and the presence of downer cow protocols, we assessed decisions regarding euthanasia and use of behavioral prognostic indicators. Written downer cow protocols were reported by 18.2% of respondents, 67% indicated that they had a nonwritten protocol, and 14.8% reported that they did not have a protocol (either written or nonwritten). Respondents from western provinces were more likely to have a written protocol than those from Ontario. Nineteen percent of the respondents with a written or unwritten protocol reported veterinary involvement in developing their downer cow protocol, which occurred more commonly on farms with more frequent herd health visits and a good producer-veterinarian relationship. An area to move a downer cow to was present on 88% of farms, with respondents who were farm staff being less likely to report having knowledge of a designated area than respondents who were the farm owner. In addition, approximately half (45%) of respondents reported moving downer cattle with hip lifters as their most common method. Behavioral prognostic indicators chosen by respondents were associated with the respondent's geographic region, age, farm size, and education. Most notably, older respondents were more likely to use appetite, and less likely to use attitude, as a prognostic indicator compared with younger respondents. Using perceived pain as a prognostic indicator was more common among respondents from western and Atlantic provinces compared with respondents from Ontario, and more common among respondents with a college or university education. These results highlighted herd and farmer demographics that were associated with how Canadian dairy producers managed downer cattle in 2015 and could be used as a benchmark for evaluating how these management practices compare with those currently implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".