Exploring the characteristics and dynamics of Ontario dairy herds experiencing increases in bulk milk somatic cell count during the summer
Bibliographic record
Abstract
Regionally aggregated bulk milk somatic cell count (BMSCC) data from around the world shows a repeatable cyclicity, with the highest levels experienced during warm, humid seasons. No studies have evaluated this seasonal phenomenon at the herd level. The objectives of this study were to define summer seasonality in BMSCC on an individual herd basis, and subsequently to describe the characteristics and dynamics of herds with increased BMSCC in the summer. The data used for this analysis were from all dairy farms in Ontario, Canada, between January 2000 and December 2011 (n≈4,000 to 6,000 herds/yr). Bulk milk data were obtained from the milk marketing board and consisted of bulk milk production, components (fat, protein, lactose, other solids), and quality (BMSCC, bacterial count, inhibitor presence, freezing point), total milk quota of the farm, and milk quota and incentive fill percentage. A time-series linear mixed model, with random slopes and intercepts, was constructed using sine and cosine terms as predictors to describe seasonality, with herd as a random effect. For each herd, seasonality was described with reference to 1 cosine function of variable amplitude and phase shift. The predicted months of maximal and minimal BMSCC were then calculated. Herds were assigned as low, medium, and high summer increase (LSI, MSI, and HSI, respectively) based on percentiles of amplitude in BMSCC change for each of the 4 seasons. Using these seasonality classifications, 2 transitional repeated measures logistic regression models were built to assess the characteristics of MSI and HSI herds, using LSI herds as controls. Based on the analyses performed, a history of summer BMSCC increases increased the odds of experiencing a subsequent increase. As herd size decreased, the odds of experiencing HSI to MSI in BMSCC increased. Herds with more variability in daily BMSCC were at higher odds of experiencing MSI and HSI in BMSCC, as were herds with lower annual mean BMSCC. Finally, a negative association was noted between filling herd production targets and experiencing MSI to HSI in BMSCC. These findings provide farm advisors direction for predicting herds likely to experience increases in SCC over the summer, allowing them to proactively focus udder health prevention strategies before the high-risk summer period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".