How benchmarking motivates farmers to improve dairy calf management
Bibliographic record
Abstract
Dairy calves often receive inadequate colostrum for successful transfer of passive immunity and inadequate milk to achieve their potential for growth and avoid hunger, but little is known about what motivates farmers to improve calf management around these concerns. Our aim was to assess if and how access to benchmarking reports, providing data on calf performance and peer comparison, would change the ways in which farmers think about calves and their management. During our study, 18 dairy farmers in the lower Fraser Valley (British Columbia, Canada) each received 2 benchmark reports that conveyed information on transfer of immunity and calf growth for their own calves and for other farms in the region. Farmers were interviewed before and after receiving their benchmarking reports to gain an understanding of how they perceived access to information in the reports. We conducted qualitative analysis to identify major themes. Respondents generally saw having access to these data and peer comparisons favorably, in part because the reports provided evidence of how their calves were performing. Benchmarking encouraged farmers to make changes in their calf management by identifying areas needing attention and promoting discussion about best practices. We conclude that some management problems can be addressed by providing farmers better access to data that they can use to judge their success and inform changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".