Estado actual y aplicaciones de la transferencia de embriones en bovinos
Bibliographic record
Abstract
Los productores de ganado en el mundo aprecian las ventajas de la inseminación artificial (IA). La implementación de esta tecnología en rodeos de leche es una opción de selección genética que ha resultado en un significante aumento en la producción de leche por animal. Aunque la IA es quizás la biotecnología más costo/efectiva hasta el momento, la contribución genética materna permanece sin explotar. Por el contrario, cuando se aplica la transferencia de embriones, la parte materna tiene una considerable influencia sobre las tasas de respuesta genética. La implementación de esta tecnología permite acelerar la ganancia genética con la contribución de ambos sexos. Además, los productores comerciales tanto de ganado productor de carne como de leche se pueden beneficiar con programas de transferencia de embriones bien diseñados, con criterios de selección apropiados a sus medio ambientes y objetivos individuales (Seidel, 1981; Hasler, 2003).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".