Implementation of genomic evaluation for digital dermatitis in Canada
Bibliographic record
Abstract
Digital dermatitis represents the most prevalent hoof lesion in Canada, with almost 20% of cows affected. A data collection system of hoof lesions, which uses standardized and reliable scores, was developed in Canada within a four-year project started in 2014. Hoof trimmers willing to share data and to develop a standard protocol were identified across Canada. Consecutively, a pipeline for a routine flow of hoof lesion records from hoof trimmers to Canadian DHI and to Canadian Dairy Network (CDN) was developed. The data collected through this pipeline were then used to develop a herd management report provided by DHI, and a national genomic evaluation for digital dermatitis offered by CDN. The genomic evaluation was introduced in December 2017, using hoof lesions recorded by hoof trimmers between 2006 and 2017. Heritability and repeatability estimates for digital dermatitis were 0.08 and 0.20, respectively. Breeding values were estimated for Holstein cattle with a univariate linear animal model. Other possible indicator traits for digital dermatitis, such as selected conformation traits, were not included due to low genetic correlations, and low contribution to increase in prediction reliability. Single-step genomic evaluation was implemented using a reference population of 19,459 animals (5,268 sires and 14,191 cows, respectively). The average reliability for bulls in the reference population was 77%. Correlations between GEBV for resistance to digital dermatitis and traits currently under selection were all favorable.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".