Longitudinal study of the effects of teat condition on the risk of new intramammary infections in dairy cows
Bibliographic record
Abstract
Machine milking–induced alterations of teat tissue may impair local defense mechanisms and increase the risk of new intramammary infections. The objective of the current study was to assess the influence of short-term and long-term alterations of teat tissue and infectious status of the udder quarter on the risk of naturally occurring new intramammary infections, inflammatory responses, and mastitis. Short-term and long-term changes in teat condition of right udder quarters of 135 cows of a commercial dairy farm in Saxony-Anhalt, Germany, were recorded monthly for 10 mo using simple classification schemes. Quarter milk samples were collected from all examined quarters at each farm visit. Bacteriological culture results and somatic cell counts of quarter milk samples were used to determine new inflammatory responses (increase from ≤100,000 cells/mL to >100,000 cells/mL between 2 samples), new infections (detection of a pathogen from a quarter that was free of the same pathogen at the preceding sampling), and new mastitis (combination of new inflammatory response and new infection). Separate Poisson mixed models for new inflammatory responses, new infections, and new mastitis caused by specific pathogens or groups of pathogens (contagious, environmental, major, minor, or any) were used to estimate risk ratios and 95% confidence intervals. Data preparation and parameter estimation were performed using the open source statistical analysis software R. We observed no effect of any variable describing teat condition on the risk of new intramammary infections, inflammatory responses, or mastitis. Intramammary infections of the same udder quarter in the preceding month did not affect risk either.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".