A coordinated udder health training strategy in Quebec, Canada
Bibliographic record
Abstract
An udder health training program was developed to integrate the roles of veterinarians, farm advisors and dairy producers in a team approach to improving udder health in Quebec, The program, inspired by the Dutch Udder Health Center’ (UGCN) program, was a collaboration of the Quebec Association of Bovine Practitioners (AMVPQ), Valacta, the Canadian Bovine Mastitis Research Network, the Faculty of Veterinary Medicine-University of Montreal and the Dairy Producers Federation of Quebec. Three levels of training workshops for veterinarians, for farm advisors and for producers were conducted from April 2009 to April 2010. Bovine practitioners and local Valacta farm advisors collaborated to deliver producer workshops. The UGCN training kit was adapted in cooperation with UGCN to produce the TACTIC Udder Health Kit for Canadian conditions. TACTIC and USB memory devices with printable udder health evaluation, planning and technical information tools for use on-farm were distributed to practitioners. Half of Quebec bovine practitioners participated in advanced workshops and received TACTIC. Next, 95% of Valacta advisors received training with an adapted version of the kit, and 30% of Quebec dairy producers attended fee-based workshops team-led by local veterinary practitioners and Valacta advisors. Strengths of this strategy were: (1) clear identification of the complimentary roles and harmonization of the core messages; (2) wide dissemination of practical materials; (3) involvement of local advisors in farmer training; and (4) adaption of farmer training to local farm conditions using animal health and milk recording data.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".