Evaluación de la Genealogía de Embriones Implantados Mediante Programas de Transferencia de Embriones
Bibliographic record
Abstract
Genealogy studies are born out of an interest in clarifying the dilemma “where we come from and where we are going.” In this situation, the records of sire, dam and grandparents (paternal and maternal) are consolidated, thus initiating a genealogical tree. The lack of consolidation of the genealogical registry in the production systems becomes an impediment for the pedigree analysis and the performance of genetic evaluations. The objective this work is to evaluate the genealogy of the animals involved in the matings for the generation of implanted embryos, using the embryo transfer technique in the GESTAR project. The database had 741 animal records and a depth of 5 generations. Softwares, Pedigree Viewer version 6.5 and CFC: Tool For Monitoring Genetic Diversity version 1.0 were used to perform genealogical analysis, renumber the records of the individuals and evaluate the depth of the pedigree. Results and conclusions: Of the 741 records of animals that comprise 5 generations, 551progeny were identified, 181 father’s records and 282 records of mothers classified in 184 founding individuals and 557 non-founders, only 6 individuals with father identification and 551 with father and known mother, 74 groups of complete siblings with an average size per family of 3.72 animals, a maximum of 20 and a minimum of 2 and finally 42 of them were identified as consanguineous. The evaluated genealogy presents a high connectivity between individuals and has adequate depth to be used in genetic improvement or embryo transfer programs, thus increasing the reliability of breeding values and other parameters of interest.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".