Alternative somatic cell count traits to improve mastitis resistance in Canadian Holsteins
Bibliographic record
Abstract
The objective of this study was to investigate whether alternative somatic cell count (SCC) traits are suitable as mastitis indicators in Canadian Holsteins. Mastitis data recorded by producers were available from the national dairy cattle health system in Canada. Mastitis was defined as a binary variable based on whether or not the cow had at least one mastitis case in the period from calving to 305 d after calving. The analyzed alternative SCC traits included mean somatic cell score (SCS) from different time periods, maximum SCS, standard deviation of SCS, excessive test-day SCC, and a peak pattern of test-day records with suspicion of mastitis. Data of 53,626 first-lactation Holstein cows from 1,666 herds across Canada were analyzed using linear animal models. A heritability of 0.02 was obtained for mastitis. For both mean SCS in early and late lactation, a heritability of 0.11 was estimated. Heritabilities of various patterns of SCC ranged from 0.01 to 0.07. Estimated genetic correlations were 0.69 and 0.68 between mastitis and mean SCS in early and late lactation, respectively. Higher genetic correlations were found between mastitis and the different SCC patterns (0.82 to 0.91). Sires with high breeding values for mastitis resistance had consistently higher percentage of healthy daughters than sires with low breeding values for mastitis resistance. Breeding values for mean SCS in early lactation, standard deviation of SCS, and an excessive test-day SCC pattern (at least one SCC test-day above 500,000) were the best predictors of the breeding value for mastitis resistance and explained in total 41% of the variation in relative breeding values for mastitis resistance. The results demonstrated that patterns of SCC provide additional information for genetic evaluations of mastitis resistance that cannot be explained by mean SCS alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".