Quarter‐milking‐, quarter‐, udder‐ and lactation‐level risk factors and indicators for clinical mastitis during lactation in pasture‐fed dairy cows managed in an automatic milking system
Bibliographic record
Abstract
OBJECTIVES: To identify risk factors and indicators at quarter-milking and other levels for clinical mastitis during lactation in pasture-fed cows managed in an automatic milking system. DESIGN: A retrospective nested case-control study using data collected over a 31-month period. A total of 803,948 quarter-milkings from 249 cows were available for analysis, of which 245 were selected as clinical mastitis quarter-milking cases and 2450 unaffected quarter-milkings were selected as controls. Most of the diagnoses of clinical mastitis had been made after clinical assessments of quarters following automated alerts by the milking system. Potential risk factors and indicators were assessed using univariable and multivariable multilevel logistic models. RESULTS: Quarters that are milked infrequently and have low yield, fast peak milk flow rates, blood in the milk and/or elevated milk conductivity are at increased risk of clinical mastitis. Quarters were also at increased risk between days 10 and 29 of lactation, and during higher parity lactations. Milk fat and protein concentrations and the fat:protein ratio were not significantly associated with the development of clinical mastitis. Neither quarter position (fore or rear) nor side (left or right) was associated with clinical mastitis occurrence. Quarters that had been clinically affected more than 30 days previous in the current lactation were not at increased risk of redeveloping clinical mastitis. CONCLUSIONS: Under the study conditions, the risk of clinical mastitis increased for quarter-milkings in quarters that were milked less frequently in the previous 7 days, were low-yielding, had fast peak milk flow rates and had more milkings in which blood was detected in milk and/or the milk conductivity was elevated. Risk was also increased in days 10-29 of lactation and in higher parity lactations.
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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) | 0.001 | 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".