Short Communication: Comparison of Bulk Milk, Yield-Corrected, and Average Somatic Cell Counts as Parameters to Summarize the Subclinical Mastitis Situation in a Dairy Herd
Bibliographic record
Abstract
In this study, the correlation was determined between the prevalence of high cow-level somatic cell count (SCC >250,000 cells/mL), a summary of the subclinical mastitis situation in a dairy herd, and 3 average herd SCC parameters: bulk milk SCC (BMSCC), yield-corrected test-day SCC (CHSCC), and the arithmetic average test-day SCC (HSCC) of the lactating herd. The herd prevalence of cows with an SCC of >250,000 cells/mL was calculated by using Dairy Herd Improvement data. Herds were included if BMSCC was sampled within 2 d of the Dairy Herd Improvement test day and if the BMSCC did not exceed 400,000 cells/mL. The interval between sampling, 0, 1, or 2 d, did not significantly influence the correlation between BMSCC and the prevalence of high SCC. The correlations between the prevalence of high SCC and BMSCC, yield-corrected test-day SCC, and HSCC, examined by using a linear regression model, were 0.64, 0.78, and 0.89, respectively. Therefore, it can be concluded that, based on the highest correlation, HSCC is a more appropriate parameter than BMSCC to summarize the average herd subclinical mastitis situation in a dairy 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".