A national research network to promote mastitis control in Canada
Bibliographic record
Abstract
Acquiring new knowledge and techniques by dairy farmers is critical to controlling mastitis. The Canadian Bovine Mastitis Research Network is a unique partnership that aims to advance mastitis control through integrated mastitis research and transfer. The Canadian dairy industry and 42 researchers in ten institutions organized the Network with a major objective of rapid and comprehensive transfer of its research results to producers. The Network funding model comprises foundational support from dairy producer organizations across Canada with substantially augmented governmental research funding. The Network programs research in consultation with industry to resolve gaps in knowledge and application and to optimize data collection, research resource sharing, and training of highly qualified personnel. The dairy industry participates in the Network 1) by providing leadership on the Board of Directors, 2) by contributing to scientific planning and evaluation, 3) by guiding the transfer of information to the industry, and 4) by collaborating in Network research projects. The industry’s involvement in the Network program stimulates a sense of ownership and identity with the program, and an awareness and expectation of applicable research results. Governmental funding gives added-value to the industry’s investment. The Network permits research on a national level and will produce results that reflect the mastitis situation across the full breadth of Canadian dairy production environments. This will enhance applicability and producer receptivity of the information and technology transferred by the Network.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".