Management Practices and Heifer Characteristics Associated with Early Lactation Somatic Cell Count of Belgian Dairy Heifers
Bibliographic record
Abstract
Associations between somatic cell counts (SCC) from heifers between 5 and 14 d in milk (DIM) and both herd-management practices and heifer characteristics were studied for 1912 heifers in 159 dairy herds in Flanders (Belgium). In higher-producing herds and in herds with an average calving age of heifers > 27 mo, SCC of heifers was lower than in less-producing herds or in herds with an average calving age < or = 27 mo. Heifers raised in herds with a higher bulk-milk somatic cell count had higher SCC. In herds in which heifers calved on slatted floors, heifers had lower SCC compared with herds in which heifers calved on nonslatted floors. A significant difference in SCC was observed between provinces. At the heifer level, SCC decreased with increasing DIM. On average, heifers calving in April to June had higher SCC, compared with those calving in the other months of the year. In the multilevel regression models, nearly all the variations of SCC resided at the heifer level, indicating that preventive measures against udder health problems in freshening dairy heifers should, in the short term, focus more on factors that vary between heifers than on factors that vary between herds. However, for the long term, the need to identify new, and to implement known, herd-level strategies is important.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".