Effect of Herd Characteristics, Management Practices, and Season on Different Categories of the Herd Somatic Cell Count
Bibliographic record
Abstract
In this study, the contribution of management practices, herd characteristics, and seasonal variables to the herd somatic cell count (SCC) was quantified in herds with low (<150,000 cells/mL), medium (150,000-200,000 cells/mL), and high (>200,000 cells/mL) herd SCC (HSCC). Selection of the variables was performed using a linear mixed effect model; HSCC was calculated as the arithmetic mean of the individual cow's SCC. The data concerning management practices were derived from 3 questionnaires on mastitis prevention and management practices on 246 Dutch dairy farms. The monthly Dairy Herd Improvement test data of these 246 farms were used to calculate the herd characteristics and seasonal effects. None of the management practices were associated with HSCC in all 3 HSCC categories. Some variables only had a significant association with HSCC in one HSCC category, such as dry premilking treatment (-9,100 cells/mL in the low HSCC category) or feeding calves with high SCC milk (11,100 cells/ mL in the medium HSCC category). Others had an opposite effect on HSCC in different HSCC categories, such as average parity (-6,400 and 11,000 cells/mL in the low and medium HSCC category, respectively) and feeding calves with fresh milk (10,300 and -9,700 cells/ mL in the low and high HSCC category, respectively). We conclude that, given the individual Dairy Herd Improvement data and information on management practices of an individual farm, it is possible to provide quantitative insight into the contribution of these different variables to the HSCC of an individual farm. Being able to provide such insight is a prerequisite for interpretation, prediction, and control of HSCC on individual dairy farms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".