PSVIII-7 Genetic parameters for health traits in dairy calves
Bibliographic record
Abstract
Abstract Recent issues in the dairy industry related to both animal and public health concerns are leading farmers away from the use of drugs, while placing more focus on animal health and welfare. Public demands are also shifting towards ensuring socially acceptable production practices in terms of good animal health and welfare. Such challenges are moving the focus in dairying from solely financial to a broader set of themes that, once addressed, will enhance the sustainability of dairying and provide a long-term competitive advantage for the Canadian industry. Furthermore, from a production standpoint, calf diseases, such as, diarrhea and respiratory disease (RD) have been associated with decreased first lactation production and growth rate, therefore decreasing an animal’s potential lifetime profitability. As part of a larger project aiming to add calf health traits to genetic evaluations in Canadian dairy cattle, this study provides the groundwork through the estimation of genetic parameters of two calf health traits, diarrhea and RD. Data were provided by Lactanet Canada, and included 20,594 calf records for diarrhea from 741 herds, and 48,927 calf records for RD from 1,412 herds, recorded between 2004 and 2021 across Canada. Total herd records ranged between 1 and 3,860 for RD with an average of 37 records per herd, while for diarrhea records ranged between 1 and 3,724 with an average of 28 records per herd. The results of this study will be used to optimally fit both diarrhea resistance and RD resistance into a novel resiliency index for use in national genetic evaluations in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".