Association Between Somatic Cell Count in Early Lactation and Culling of Dairy Heifers Using Cox Frailty Models
Bibliographic record
Abstract
The association between somatic cell count (SCC) of dairy heifers in early lactation [SCCel; measured between 5 and 14 d in milk (DIM)] and the culling hazard during the first lactation was studied using Cox frailty models. Udder health problems were the culling reason for 10% of the culled heifers in this study. For each unit increase in the log-transformed SCCel (LnSCCel), the culling hazard increased by 11% [Hazard ratio (HR) = 1.11]. The strength of the association depended on 5 factors. Firstly, the association was stronger when SCCel was recorded after 10 DIM than at an earlier DIM. Secondly, the association was stronger if only culling events for udder disorders were considered (HR = 1.32) instead of all culling events (HR = 1.11). Furthermore, for each unit increase of test-day LnSCC after 14 DIM, modeled as a time-varying covariate, the culling hazard in the first lactation increased by 26% (HR = 1.26). Including LnSCC in the model already containing LnSCCel, reduced the estimate of LnSCCel slightly. Fourth, a higher test-day milk yield, modeled as a time-varying covariate, protected against culling and reduced the magnitude of the effect of LnSCCel as well when taken into account. Finally, the association between LnSCCel and culling was still present, although smaller, in the group of heifers with a second test-day SCC </=50,000 cells/mL.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".