Views of Western Canadian dairy producers on calf rearing: An interview-based study
Bibliographic record
Abstract
Calf rearing practices differ among farms, including feeding and weaning methods. These differences may relate to how dairy producers view these practices and evaluate their own success. The aim of this study was to investigate perspectives of dairy producers on calf rearing, focusing on calf weaning and how they characterized weaning success. We interviewed dairy producers from 16 farms in Western Canada in the following provinces: British Columbia (n = 12), Manitoba (n = 2), and Alberta (n = 2). Participants were asked to describe their heifer calf weaning and rearing practices, and what they viewed as successes and challenges in weaning and rearing calves. Interviews were recorded, transcribed, and subjected to qualitative analysis from which we identified the following 4 major themes: (1) reliance on calf-based measures (e.g., health, growth, and behavior), (2) management factors and personal experiences (e.g., ease, consistency, and habit), (3) environmental factors (e.g., facilities and equipment), and (4) external support (e.g., advice and educational opportunities). These results provided insight into how dairy producers view calf weaning and rearing, and may help inform the design of future research and knowledge transfer projects aimed at improving management practices on dairy farms.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".