Relationship between female fertility and production traits in Canadian Holsteins
Bibliographic record
Abstract
The objectives of this study were a) to estimate the genetic correlation between milk production and some female fertility traits such as 56-d nonreturn rate in cows (NRRC), calving to first service (CTFS), and first service to conception (FSTC); b) to assess the influence of including milk production as a correlated trait on the genetic evaluation of these traits in Canadian Holsteins; and c) to determine if using heifer nonreturn rate (NRRH) had a similar effect as using milk production on cow NRRC evaluation. The data included fertility and production records of first-parity Holstein cows. Genetic parameters were estimated using uni- and bivariate analyses in which milk production at around 90 DIM (TD90M) was included as a correlated trait to NRRC, CTFS, and FTSC. A bivariate analysis was also carried out in which NRRH was included as a correlated trait to NRRC. The models were compared by genetic trend (NRRC, CTFS, and FSTC) and cross-validation and predictability (NRRC). The heritability estimates for NRRC from the uni- and bivariate analyses were 0.017 and 0.020, respectively. The corresponding figures for CTFS were 0.07 and 0.08 and for FSTC were 0.049 and 0.05. The genetic trends for NRRC of the 2 models (NRRC+TD90M and NRRC+NRRH) gave very similar results. However, when milk production was included in the genetic evaluation of CTFS and FSTC, the genetic trends of the 2 fertility traits were higher compared with the univariate analysis. In NRRC evaluation by cross-validation and predictability, the bivariate analyses were more consistent and gave a better predictability than the univariate analysis. However, there was no major difference between the 2 models. Consequently, it might be worth including milk production or heifer fertility as correlated traits in the genetic evaluation of female fertility traits.
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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 | 0.002 |
| 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 | 0.001 |
| 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".