Studies on disease resistance based on producer-recorded data in Canadian Holstein cattle
Bibliographic record
Abstract
Health traits are some of the most important cost factors in dairy cattle production. Eight important health traits were chosen for data collection in Canada. They were mastitis, lameness, cystic ovarian disease, left displaced abomasum, ketosis, metritis, milk fever, and retained placenta. Data collected by producers on these 8 diseases were stored in a central database. These recordings were the basis to prepare genetic evaluations for health in Canada. Effect of the quality of the data was analyzed by using 2 different sampling frames for the inclusion of herds in the analysis: a stringent sampling frame requiring all herds to have collected at least one case of the disease analyzed and a second sampling frame requiring herds to have collected one case of any disease. Variance components were estimated with a linear model. Heritability estimates of all health traits were lower than 0.03. The second sampling frame gave lower estimates than the first one. Correlations between predicted transmitted abilities (PTA) calculated with both sampling frames were higher than 0.9. A second analysis compared the effects of using a threshold model instead of a linear model. Health traits were also grouped according to biological aspects. Heritability estimates calculated with the threshold model were higher than those of the linear model, but when they were transformed to the observable scale, results from both modelling approaches were similar. Use of indicator traits was investigated in analyzing body condition score (BCS) and health traits simultaneously. A longitudinal and a multiple-trait approach were used. BCS was positively correlated with resistance to disease, except for lameness, where a negative correlation was found. Heritability of BCS was moderate and selection for this trait would improve disease resistance. Finally, a survey was sent to producers to assess data collection practice. Most of the producers collecting health data were collecting data on mastitis. On the other hand, only 50% of producers collected data on lameness, cystic ovarian disease, ketosis or metritis. Awareness for health data collection should be raised through extension work.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".