Chronic subclinical mastitis reduces milk and components yield at the cow level
Bibliographic record
Abstract
We evaluated the effects of chronic subclinical mastitis (CSM) caused by different types of pathogens on milk yield and milk components at the cow level. A total of 388 Holstein cows had milk yield measured and were milk sampled three times at intervals of two weeks for determination of SCC and milk composition, and microbiological culture was performed. Cows were considered healthy if all three samples of SCC were ≤200 000 cells/ml and were culture-negative at the third milk sampling. Cows with one result of SCC > 200 000 cells/ml were considered to suffer non-chronic subclinical mastitis whereas cows with at least 2 out of 3 results of SCC > 200 000 cells/ml had CSM. These latter cows were further sorted according to culture results into chronic negative-culture or chronic positive-culture. This resulted in four udder health statuses: healthy, non-chronic, chronicNC or chronicPC. The milk and components yields were evaluated according to the udder health status and by pathogen using a linear mixed effects model. A total of 134 out of 388 cows (34.5%) were chronicPC, 57 cows (14.7%) were chronicNC, 78 cows (20.1%) were non-chronic and 119 cows (30.7%) were considered healthy, which resulted in a grand total of 1164 cow records included in the statistical model. The healthy cows produced more milk than each of the other groups (+2.1 to +5.7 kg/cow/day) and produced higher milk component yields than the chronicPC cows. The healthy cows produced more milk than cows with chronicPC caused by minor (+5.2 kg/cow/day) and major pathogens (+7.1 kg/cow/day) and losses varied from 5.8 to 11.8 kg/cow/day depending on the pathogen causing chronicPC mastitis. Chronic positive-culture cows had a reduction of at least 24.5% of milk yield and 22.4% of total solids yield.
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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 | 0.001 |
| 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".