PSV-2 Difference in Sire Pta Evaluated By Taiwan Dhi and the Original Countries
Bibliographic record
Abstract
Abstract Taiwan is located in a region with a tropical/subtropical oceanic climate characterized by high temperatures and humidity, which significantly affects the milk production, health, and reproduction of dairy cows. Most dairy herds in Taiwan have utilized semen of superior foreign sires from countries in temperate climate, mainly from USA and Canada. However, the performance of daughters in Taiwan of those foreign sires may differ from those in temperate countries. Therefore, the objective of this study was to evaluate the performance of foreign sire semen in Taiwan's dairy herds, and to compare the PTAs of those sires in both Taiwan and their original countries. Data and pedigree from the Dairy Herd Improvement program (DHI) were collected between 2009 and 2021. In total, 108,348 lactation records from 54,269 cows and 76,024 animals in the pedigree were obtained from 120 herds. Traits to evaluate include milk yield (MY), milk fat percentage (MF%), milk fat yield (MFY), milk protein percentage (MP%), and milk protein yield (MPY) standardized to 305d-2X yield. The herd-year-season and lactation were considered as fixed effects in the model with additive genetic effect and permanent environmental effect as random effects. In addition, an extra fixed effect, physical situation of lactation (PSL), was included to account for abnormal physiology or subclinical mastitis in those dairy cows. The ASReml program was used to estimate the variance and covariance components as well as heritability and correlation with multiple-trait animal models. Not only the accuracy of predicted transmitting ability (PTATW) ≥ 75% of those superior foreign sires using DHI data and their PTAs obtained from the original countries (PTAInt) were compared, but also the rank correlation between PTATW and PTAInt was also calculated. The results indicated that only when the accuracy of PTATW increased to 90%, a moderate correlation was observed. Moreover, PTATW and PTAInt of all the sires with an accuracy of PTATW ≥ 75% were tracked to the lactation performance of their daughters in Taiwan using regression analysis. The results showed that selecting sires with an increase of 1 kg in PTATW and in PTAInt would increase the average production of their daughters in Taiwan's dairy herds by 1.620 kg and 0.213 kg in milk yield, 1.328 kg and 1.201 kg in fat yield, and 1.493 kg and 0.550 kg in protein yield, respectively. Therefore, selecting offspring through PTATW of sires is much more efficient, and increasing the number of daughters for each sire and their DHI records would improve the accuracy of PTATW prediction and ranking, making it an important indicator for selecting progeny in Taiwan dairy herds.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".