Short communication: Bulk milk somatic cell penalties in herds enrolled in Dairy Herd Improvement programs
Bibliographic record
Abstract
The objective of this study was to determine the effect of somatic cell count (SCC) monitoring at the cow level through Dairy Herd Improvement (DHI) programs on the risk of bulk tank SCC (BTSCC) penalties. For the year 2009, BTSCC for all producers in Ontario were examined, for a total of 2,898 DHI herds, 1,186 non-DHI herds, and 48,250 BTSCC records. Two penalty levels were examined, where BTSCC exceeded 499,000 (P500) and 399,000 (P400) cells/mL. Data were modeled first to determine the odds of a BTSCC exceeding a set penalty threshold and second to determine the odds of incurring a penalty under the Ontario Milk Act. All data were modeled as a generalized mixed model with a binary link function. Random effects included herd, fixed effects included season of BTSCC (summer, May to September, and winter, October to April), total milk shipped per month (L), fat paid per month (kg), protein paid per month (kg), and participation or not in the DHI program. The likelihood of a BTSCC exceeding a penalty threshold in a non-DHI herd compared with a DHI herd was significantly greater than 1 at both penalty levels, where the odds ratios were estimated to be 1.42 [95% confidence interval (CI): 1.19 to 1.69] and 1.38 (95% CI: 1.25 to 1.54) for P500 and P400, respectively. The likelihood of incurring a BTSCC penalty (where 3 out of 4 consecutive BTSCC exceeded penalty thresholds) was not significantly different at P500; however, it was significantly different for P400, where the odds ratio was estimated to be 1.42 (95% CI: 1.12 to 1.81).
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".