Short communication: Genetic parameters for mastitis and its predictors in Canadian Holsteins
Bibliographic record
Abstract
The objective of this study was to estimate genetic parameters for mastitis and its predictors [mean somatic cell score (SCS) in early lactation, standard deviation of SCS, excessive test-day somatic cell count (SCC), udder depth (UD), fore udder attachment (FUA), and body condition score (BCS)]. 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 1 mastitis case in the period from calving to 305 d after calving. A Bayesian analysis using Gibbs sampling was applied. Threshold liability models were applied for binary traits (mastitis and excessive test-day SCC), and linear models were used for other normally distributed traits. For mastitis, a heritability of 0.07 was obtained. Heritability estimates for mean SCS in early lactation, standard deviation of SCS, excessive test-day SCC, UD, FUA, and BCS were 0.10, 0.04, 0.06, 0.41, 0.21, and 0.18, respectively. Mastitis was highly correlated with mean SCS in early lactation (0.63), standard deviation of SCS (0.74), and excessive test-day SCC (0.76). Moderate genetic correlations of -0.36, -0.24, and -0.28 were found between mastitis and UD, FUA, and BCS, respectively. As much as 72% of the genetic variation in mastitis resistance was explained by all the indirect predictor traits, whereas the most commonly used indirect measures of mastitis resistance (SCS in early lactation, UD, and FUA) explained together only 46% of the genetic variation in mastitis resistance. A combination of mean and standard deviation of SCS seem to be more successful in improving udder health than the traditional indirect measures. The results of the present study highlight that although routine cow SCC is the best measurement to monitor udder health, it cannot explain all the genetic variation in mastitis resistance and, therefore, direct information on mastitis resistance can be expected to yield to a more accurate genetic evaluation for this trait.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".