Reassessing cow comfort measures on Canadian dairy farms after a recommendation of improvements
Bibliographic record
Abstract
In a previous study (Assessment 1), an on-farm assessment tool was used to establish a standard across 60 Quebec dairy farms using animal-, stall-, and management-based measures of cow comfort. The objective of the present follow-up study (Assessment 2) was to perform a reassessment using an identical cow comfort assessment tool on a subset of the original farms expected to benefit most from applying the recommended changes, and to determine the effects of the modifications on cow comfort. From the subsample of farms, 24 reported applying stall modifications based on recommendations from Assessment 1 with the aim of promoting cow comfort (Adopters), while the remaining 10 farms did not (Non-adopters). The assessment tool included 19 target areas, grouped into 9 critical areas based on measures of cow comfort that considered housing, feed-water, health, and welfare. The on-farm report of Assessment 1 was found to increase producer awareness of issues in cow comfort measures on farms, as shown by an improvement in stall design according to recommendations based on cow body dimensions and reduced injury prevalence. Routine reassessment of cow comfort measures may be important to use in supporting target achievement improvements as part of welfare improvement strategies.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".