Análisis de la diversidad genética de la población de toros Holstein Friesian importados al Ecuador entre los años 2000-2021.
Bibliographic record
Abstract
Genetic diversity ensures the animal populations evolution and adaptation. Therefore assesing the genetic relationships, among others, imported and currently marketed bulls in Ecuador, through genealogical information, it is the research aim. As of digital and physical catalogs of bovine semen trading enterprises, it was used 273 bulls imported to Ecuador that are available, between the 2000-2021 years. It was got genealogical information for all Bulls up to four generations. For the flow and genetic relationships análisis, they were used information corresponding to name, international code (ID), country imported bull (DOB) and its ancestors birth date, both paternal (SIRE) and maternal (DAM), considering the queries in the ancestor origin countries databases, into four generations. It was assessed the ages, percentile value for net merit, the inbreeding by pedigree and genomics. The descriptive statistical análisis is performed by using the INFOSTAT program. The inbreeding coefficient and the average kinship with the ENDOG v4.8 program. Holstein Friesian genetics from the United States and Canada is responsible for 89.25% of the genetic flow to Ecuador. The assessed bulls average age was 8.50 years, assuming a generational interval between 6-8 years. Within the Ecuadorian market there is great proven Bulls in proof use, affecting the generation interval and genetic progress in comparison to the genomis bull´s, use. Regarding consanguinity, there is a high percentage index (20% - 13%) between the 2012 – 2016 years. In relation to net merit, the most the bulls are less than the 50th percentile, being the most used.
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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 | 0.002 | 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".