A survey of practices implemented to improve cow comfort following an initial assessment on Canadian dairy farms
Bibliographic record
Abstract
The objectives of this study were to determine the difficulty of implementing changes to improve cow comfort on Canadian dairy farms, to determine if any changes were implemented to improve dairy cow comfort following an initial cow comfort assessment, to categorize producers based on types of changes they made, to compare how producers in these categories differed, and to identify barriers to implementing these changes. The most difficult type of change to implement was changing stall design (including building a new barn) with a mean difficulty score of 3.3 (out of 5) scored by a panel of dairy researchers. Overall, 3 of 118 (2.5%) interviewed producers were categorized as innovators, 62 (52.5%) as effective adopters, 20 (16.9%) as ineffective adopters, and 33 (28.0%) as non-adopters. The most common types of changes made were to stall management (37.3%). Participants were asked to identify all barriers to further improvement of cow comfort. The most commonly reported barriers were lack of funds (52.9%), lack of time (38.7%), and being satisfied with the level of cow comfort (31.1%). This survey study demonstrates that a cow comfort assessment can influence dairy producers to implement changes to improve cow comfort; however, certain barriers exist to implementation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".