Farmer-observed health data around calving—Genetic parameters and association with veterinarian diagnoses in Austrian Fleckvieh cows
Bibliographic record
Abstract
The objective of this study was to investigate if farmer-observed health data around calving can be used together with veterinarian diagnoses for genetic evaluations. Four diseases are recorded by farmers: retained placenta, downer cow syndrome, mastitis, and lameness. Mean disease frequencies were 4.7, 3.8, and 1.8% for retained placenta, downer cow syndrome, and mastitis, respectively. Lameness had a very low frequency (0.7%) and a preliminary analysis revealed a heritability close to zero for this trait. Therefore, lameness was not considered in the analysis. For genetic analyses, univariate and bivariate linear animal models were fitted. Heritabilities for retained placenta, downer cow syndrome, and mastitis were 0.01, 0.03, and 0.003, respectively. Genetic correlations among the investigated disease traits were low to moderate and not significantly different from zero. Pearson correlations between estimated breeding values for disease traits and other routinely evaluated traits were computed, which revealed mostly favorable relationships to fertility, maternal calving ease, muscling, and longevity. In addition, a moderate favorable association was found between mastitis and somatic cell score. Heritability estimates of farmer-observed health traits were comparable to estimates based on veterinarian diagnoses. Genetic correlations between the investigated diseases based on farmer observations and veterinarian diagnoses were almost 1, with estimates ranging from 0.98 to 0.99. These results suggest that farmer recorded health data could be used together with veterinarian diagnoses for genetic evaluations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".