Producer perceptions of manual and automated milk feeding systems for dairy calves in Canada
Bibliographic record
Abstract
As part of a cross-sectional survey, Canadian dairy producers were asked a set of questions to (1) determine factors that influenced them to continue using manual milk feeding (MMF) systems or to switch to automated milk feeding (AMF) and (2) investigate producers’ perceived advantages and disadvantages regarding both feeding systems. A total of 670 responses were received. Among respondents, 16% used AMF and 84% used MMF. The four most frequent factors that producers reported as important in motivating them to switch to AMF were to raise better calves, offer more milk to calves, reduce labor, and improve working conditions. For MMF farms, investment in equipment and group housing facilities, as well as farm size, were the primary reasons reported for their continued use of MMF systems. The principal perceived advantages of having an AMF were that calves are able to express natural behaviors and drink more milk without increased labor input. For MMF systems, the main perceived advantage was related to easier identification of sick calves. Results provide insights into factors affecting the choice of calf feeding methods by dairy producers, and improve understanding of producers’ needs and expectations regarding calf management and adoption of technology.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".