Associations between somatic cell count patterns and the incidence of clinical mastitis
Bibliographic record
Abstract
Associations between clinical mastitis (CM) and the proportional distribution of patterns in somatic cell count (SCC) on a herd level were determined in this study. Data on CM and SCC over a 12-month period from 274 Dutch herds were used. The dataset contained parts of 29,719 lactations from 22,955 cows of different parities. In total, 207,079 SCC test-days were recorded with 5719 cases of CM; 1561 cases were associated with environmental pathogens (ENV_CM), and 2681 with contagious pathogens (CONT_CM). Definitions of patterns in SCC were based on 3, 4, or 5 consecutive test-day recordings of SCC that differentiated between short or longer periods of increased SCC, and also between lactations with and without recovery. The distribution of those patterns (relative to their maximum) varied among herds. The distribution of SCC patterns was correlated with the incidence rate of CM. Herds with a relatively frequent quick recovery pattern had a 2.5 times more chance of being classified in the upper quartile for CM. These herds also had 2.1 times more chance of being classified in the upper quartile for ENV_CM but only 0.4 times for CONT_CM. Herds with a relatively frequent no recovery pattern had less chance (odds ratio=0.5) of being classified in the lower quartile for CONT_CM. Since the distributions of SCC patterns were indicative for overall, environmental and contagious CM, the necessity to introduce pathogen-specific mastitis control programs in a herd could be determined based on the mean incidences of SCC patterns in that herd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".