The National Cohort of Dairy Farms—A data collection platform for mastitis research in Canada
Bibliographic record
Abstract
Costs and feasibility of extensive sample collection and processing are major obstacles to mastitis epidemiology research. Studies are often consequentially limited, and fundamental mastitis researchers rarely have the opportunity to conduct their work in epidemiologically valid populations. To mitigate these limitations, the Canadian Bovine Mastitis Research Network has optimized research funds by creating a data collection platform to provide epidemiologically meaningful data for several simultaneous research endeavors. This platform consists of a National Cohort of Dairy Farms (NCDF), Mastitis Laboratory Network, and Mastitis Pathogen Culture Collection. This paper describes the implementation and operation of the NCDF, explains its sampling protocols and data collection, and documents characteristics, strengths and limitations of these data for current and potential users. The NCDF comprises 91 commercial dairy farms in 6 provinces sampled over a 2-yr period. Primarily Holstein-Friesian herds participating in Dairy Herd Improvement milk recording were selected in order to achieve a uniform distribution among 3 strata of bulk tank somatic cell counts and to reflect regional proportions of freestall housing systems. Standardized protocols were implemented for repeated milk samplings on clinical mastitis cases, fresh and randomly selected lactating cows, and cows at dry-off and after calving. Just fewer than 133,000 milk samples were collected. Demographic and production data were recorded at individual cow and farm levels. Health management data are documented and extensive questionnaire data detailing farm management and cleanliness information are also captured. The Laboratory Network represents coordinated regional mastitis bacteriology laboratories using standardized procedures. The Culture Collection archives isolates recovered from intramammary infections of cows in the NCDF and contains over 16,500 isolates, all epidemiologically cross-referenced between linked databases. The NCDF is similar to Canadian dairies in relation to mean herd size, average production, and freestall percentages. Pathogen recovery was greater than anticipated, particularly for coagulase-negative staphylococci and Corynebacterium spp. International scientists are encouraged to use this extensive archive of data and material to enhance their own mastitis research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".