Analysis of the Relationship Between Type Traits and Functional Survival in Canadian Holsteins Using a Weibull Proportional Hazards Model
Bibliographic record
Abstract
The aim of this study was to explore the impact of type traits on the functional survival of Canadian Holstein cows using a Weibull proportional hazards model. The data set consisted of 1,130,616 registered cows from 13,606 herds calving from 1985 to 2003. Functional survival was defined as the number of days from first calving to culling, death, or censoring. Type information consisted of phenotypic type scores for 8 composite traits (with 18 classes of each) and 23 linear descriptive traits (with 9 classes of each). 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 within herd-year-parity deviations, herd-year-season of calving, each type trait, and the sire. Analysis was done one at a time for each of 31 type traits. The relative culling risk was calculated for animals in each class after accounting for the previously mentioned effects. Among the composite type traits with the greatest contribution to the likelihood function were final score, mammary system, and feet and legs, all having a strong relationship with functional survival. Cows with low scores for these traits had higher risk of culling compared with higher scores. For instance, cows classified as poor plus 1 vs. excellent plus 1 have a relative risk of culling 3.66 and 0.28, respectively. The corresponding figures for mammary system are 4.19 and 0.46 and for feet and legs are 2.34 and 0.50. Linear type traits with the greatest contribution to the likelihood function were fore udder attachment, udder texture, udder depth, rear udder attachment height, and rear udder attachment width. Stature and size had no strong relationship with functional survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".