Estimation of genetic parameters of fertility traits in Chinese Holstein cattle
Bibliographic record
Abstract
Guo, G., Guo, X., Wang, Y., Zhang, X., Zhang, S., Li, X., Liu, L., Shi, W., Usman, T., Wang, X., Du, L. and Zhang, Q. 2014. Estimation of genetic parameters of fertility traits in Chinese Holstein cattle. Can. J. Anim. Sci. 94: 281–285. The objective of this study was to estimate genetic parameters for fertility traits in Chinese Holstein heifers and cows. Data of 20169 animals with 42106 records over a period of 10 yr (2001–2010) were collected from Sanyuan Lvhe Dairy Cattle Center in Beijing, China. Traits included age at first service (AFS), number of services (NS), days from calving to first service (CTFS), days open (DO), and calving interval (CI). Genetic parameters were estimated with multiple-trait animal model using the DMU software. Heritability estimates for AFS, NS, CTFS, DO and CI were 0.100±0.012, 0.040±0.017, 0.034±0.011, 0.053±0.019 and 0.056±0.014, respectively. Genetic correlations between traits observed ranged from −0.13 to 0.99. Genetic correlations between AFS with NS, CTFS, DO and CI were −0.31, 0.15, −0.13 and −0.15, respectively. Calving interval was strongly correlated with NS, CTFS and DO (0.49–0.99), and DO showed strong correlation with NS and CTFS (0.49 and 0.58, respectively). The genetic correlation between CTFS and NS was negative moderate (−0.25). Results were in range with previous literature estimates and can be used in Chinese Holstein genetic evaluation for fertility traits.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".