Analysis of Inbreeding and Its Relationship with Functional Longevity in Canadian Dairy Cattle
Bibliographic record
Abstract
The aim of this study was to assess the level of inbreeding and its relationship to the functional survival of Canadian dairy breeds by using a Weibull proportional hazard model. Data consisted of records from 72,385 cows in 1,505 herds from 2,499 sires for Jerseys, 112,723 cows in 1,482 herds from 2,926 sires for Ayrshires, and 1,977,311 cows in 17,182 herds from 8,261 sires for Holsteins. Longevity was defined as the number of days from first calving to culling, death, or censoring. Inbreeding coefficients (F) were grouped into 7 classes (F = 0, 0 < F < 3.125, 3.125 < or = F < 6.25, 6.25 < or = F <12.5, 12.5 < or = F < 18.25, 18.25 < or = F < 25.0, and F > or = 25.0%). The statistical model included the effects of stage of lactation, season of production, the annual change in herd size, type of milk recording supervision, age at first calving, effects of milk, fat, and protein yields calculated as within herd-year-parity deviations, herd-year-season of calving, inbreeding, and sire. The relative culling rate was calculated for animals in each class after accounting for the above-mentioned effects. A trend toward increased risk of culling among more inbred animals was observed for all breeds. Little difference in survival was observed for cows with 0 < F <12.5%. The relative risk ratios (relative to F = 0) for cows with inbreeding coefficients up to 12.5% were 1.19, 1.16, and 1.14 for Jersey, Ayrshire, and Holstein cows, respectively. Greater effects of inbreeding were seen, however, when F increased beyond 12.5%.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".