Short communication: Cow- and quarter-level milking indicators and their associations with clinical mastitis in an automatic milking system
Bibliographic record
Abstract
The aim of this study was to assess associations of cow-, udder-, and quarter-level factors with the risk of clinical mastitis (CM) in cows managed using an automatic milking system. The primary hypothesis was that quarter peak milk flow rate (QPMF) is associated with increased risk of CM. A retrospective, case-control study was undertaken using data from a 1,549 cow farm using 20 automatic milking system units. All data from cows milked during March to December 2015 was available for analysis. Cases (n = 82) were defined as cows diagnosed with their first case of CM between 24 and 300 d in milk in the current lactation. Healthy control cows (n = 6/case) were randomly matched based on identical parity, existence of milk records during the day in milk period corresponding to the 15-d window before case diagnosis, average conductivity of <5.5 mS/cm in that window, and no history of CM in the current lactation. Logistic regression was used to estimate effects of parity, quarter position, day in milk at diagnosis of CM, average of QPMF 15 d before CM diagnosis, udder milk yield, and milking interval on the probability of CM. Of the 6 predictor variables included in the model, only milking interval was significantly associated with the increased risk of quarter CM. We concluded that in a high-production, freestall-housed North American herd using automatic milking system, milking interval, but not QPMF, was associated with risk of CM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".