Milk production and somatic cell counts: A cow-level analysis
Bibliographic record
Abstract
The objectives of this study were to quantify the relationship between 24-h milk loss and lactation milk loss due to mastitis at the cow level. For the year 2009, individual cow test-day production records from 2,835 Ontario dairy herds were examined. Each record consisted of 24-h milk and component yields, stage of lactation (days in milk, DIM), somatic cell count (SCC, ×10(3) cells/mL) and parity. The modeling was completed in 2 stages. In stage 1, for each animal in the study, the estimated slope from a linear regression of 24-h milk yield (kg), adjusted for DIM, the quadratic effect of DIM, and the 24-h fat yield (kg) on ln(SCC) was determined. In stage 2, the estimated slope were modeled using a mixed model with a random component due to herd. The fixed effects included season (warm: May to September, cool: October to April), milk quartile class [MQ, determined by the rank of the 24-h average milk yield (kg) over a lactation within the herd] and parity. The estimated slopes from the mixed model analysis were used to estimate 24-h milk loss (kg) by comparing to a referent healthy animal with an SCC value of 100 (×10(3) cells/mL) or less. Lactation milk loss (kg) was then estimated by using estimated 24-h milk loss within lactation by means of a test-day interval method. Lactation average milk loss (kg) and SCC were also estimated. Lastly, lactation milk loss (kg) was modeled on the log scale using a mixed model, which included the random effect of herd and fixed effects, parity, and the linear and quadratic effect of the number of 24-h test days within a lactation where SCC exceeded 100 (×10(3) cells/mL; S100). The effect of SCC was significant with respect to 24-h milk loss (kg), increasing across parity and MQ. In general, first-parity animals in the first MQ (lower milk yield animals) were estimated to have 45% less milk loss than later parity animals. Milk losses were estimated to be 33% less for animals in first parity and MQ 2 through 4 than later parity animals in comparable MQ. Therefore, the relative level of milk production was found to be a significant risk factor for milk loss due to mastitis. For animals with 24-h SCC, values of 200 (×10(3) cells/mL), 24-h milk loss ranged from 0.35 to 1.09 kg; with 24-h SCC values of 2,000 (×10(3) cells/mL), milk loss ranged from 1.49 to 4.70 kg. Lactation milk loss (kg) increased significantly as lactation average SCC increased, ranging from 165 to 919 kg. The linear and quadratic effect of S100 was a significant risk factor for lactation milk loss (kg), where greatest losses occurred in lactations with 5 or more 24-h test days where SCC exceeded 100 (×10(3) cells/mL).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".