An Expert Consensus Study Regarding Management Practices to Prevent Infectious Mortality in Preweaned Beef Calves in Western Canada
Bibliographic record
Abstract
Disease prevention is a cornerstone of herd management for minimizing preweaning calf mortality. However, scientific evidence about the usefulness of practices in herds is scarce. The first objective was for a group of veterinarians to determine which practices are most useful considering their effectiveness, ease of implementation, and economic feasibility. A second objective was for them to define which practices should be included in a tool to facilitate discussions between producers and veterinarians. Expert opinions and consensus were determined using a modified Delphi approach. During two questionnaire rounds, participants scored the effectiveness, ease of implementation, and economic feasibility of each practice. Overall scores for each practice were calculated, and feedback reports were sent to participants between rounds showing the groups' median responses. Consensus on which practices should be included in the tool was targeted during the workshops. Twelve veterinary experts participated. Administering clostridial vaccines and providing calves with colostrum in case they had not nursed were considered practices that were 'always useful for all herds'. However, most practices had intermediate levels of usefulness, and among these, antibiotics were considered the least useful. Nevertheless, all practices discussed during the workshops attained a consensus about being included in the future tool to facilitate on-farm discussions.
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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 | 0.067 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".