Quantitative analysis of participant perspectives on use of different calf handling and restraint methods for spring processing of beef calves in western Canada
Bibliographic record
Abstract
Abstract Calf handling for spring processing represents one of the few times calves are handled in extensive production systems for the purpose of welfare and productivity interventions. The objective of this study was to identify the perspectives and preferences for common beef calf handling methods from those with experience in beef production. This study is the quantitative analysis of an online survey of 863 participants in calf handling events in western Canada. The survey used video clips to highlight three common handling practices of roping and wresting (RW), roping and Nord forks (NF), and tilt tables (TT). Participants were asked to rate how acceptable it was to use a method on a 5-point Likert scale, as well as rank which method they would most prefer and least prefer to use. Additional questions included demographics, scoring empathy towards animals, factors important to animal welfare, their experience with different methods, performance of tasks within spring processing events, and factors influencing decision to use a method. The acceptability of a method had weak (TT: ρ = 0.21, p < 0.001) to no correlation (RW and NF: p > 0.05) with the preference to use the method, indicating that a method might be deemed acceptable to use but not what a participant would prefer to use for handling and restraining calves. Participants were more likely to prefer to use RW and NF if they had experience with these methods compared to those that indicated they had no experience with RW or NF (odds ratio = 7.98, 95%CI = 1.51-41.99, p = 0.01; odds ratio = 21.1, 95%CI = 3.25-138.46, p = 0.01 for RW and NF, respectively). The likelihood of ranking a method as most preferred was influenced by the tasks a participant had previously performed during processing and varied among methods. The influence of tasks an individual performed on preference to use a method highlights areas for potential innovation, particularly in the areas of needle administration with RW and NF, and calf handling and castrating with TT. Owners placed more importance on factors related to logistics than other factors when deciding which method to use (χ2 = 107.9, df = 48, p < 0.001). Best practice recommendations and assurance programs should focus on calf experience and humane handling, with guidance on how that could be achieved within different types of handling methods.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".