Impact of Early Lactation Somatic Cell Count in Heifers on Somatic Cell Counts Over the First Lactation
Bibliographic record
Abstract
The objective of this study was to estimate the impact of somatic cell count in early lactation (SCCel) from Belgian dairy heifers on test-day somatic cell count (SCC) in first lactation. Geometric mean SCCel [5 to 14 d in milk (DIM)] of the 14,766 available samples was 104,000 cells/mL, and decreased from 178,000 at 5 DIM to 74,000 cells/mL at 14 DIM. Proportion of SCCel >200,000 cells/mL was 27.5. Heifers calving in the period April–June had highest SCCel. In total, 117,496 monthly SCC were measured. A multilevel regression analysis revealed that an increase of the natural log-transformed SCCel (LnSCCel) by one unit on average resulted in an increase of test-day natural log-transformed SCC (LnSCC) by 0.22 unit. The impact of LnSCCel on LnSCC depended on when LnSCCel was measured; an elevated LnSCCel at 14 DIM was more consequential than an equally elevated LnSCCel at 5 DIM. The probability of having a test-day SCC >200,000 cells/mL during the first lactation, also increased with an increasing LnSCCel. The negative effect of an elevated LnSCCel was still present, although to a lesser extent, in heifers with a second test-day SCC ≤50,000 cells/mL. This study indicates that udder health problems in heifers in early lactation have a high prevalence and stresses that heifers should have a low SCCel, because an elevated SCCel will negatively influence test-day SCC during the whole first lactation.
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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 | 0.002 |
| 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 | 0.001 | 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".