Nongenetic effects and genetic parameters for length of productive life of Holstein cows in Hokkaido, Japan
Bibliographic record
Abstract
Records of Holstein dairy cows in Hokkaido, Japan, were used to study the effects of environmental factors on length of productive life and to estimate genetic parameters for length of productive life. Each record was assigned to 1 of 3 data sets depending on the percentage of type-scored cows in the herd. This percentage was considered to partly reflect the management policy in each herd, in particular regarding culling. The A, B, and C data sets consisted of herds with none, less than 60%, and more than 60% of type-scored cows, respectively, and included 158,719, 787,598, and 131,499 records, respectively. Analyses of length of productive life were separately carried out on each data set using the Survival Kit software (Version 5.0). Nonparametric hazards estimates and the shape parameters of the baseline Weibull distribution differed between the 3 data sets. A cow having a sire originating from the United States or Canada had a relatively lower risk of being culled than a cow having Japanese sire in data set C. However, in data set A, a cow having a Canadian sire had a higher relative risk than a cow having a Japanese sire. The herd-year variance for data set A was about twice as large as for data set C. In contrast, the sire variance for data set A was about 40% of the one for data set C. As a result, heritability varied across data sets from 0.046 to 0.134. The results of this study suggest that it is important to consider factors related to herd management policy, such as the percentage of type-scored cows, in genetic analyses on length of productive life of Holstein cows in Hokkaido, Japan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".