Genome-Wide Prediction and Selection in Plant and Animal Breeding: A Systematic Review of Current Techniques
Why this work is in the frame
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Bibliographic record
Abstract
With the advancement of genomics technology, whole genome prediction (GWP) and genome selection (GS) have become important tools in plant and animal breeding. Genomic selection utilizes whole genome marker information to select target traits through predictive models, improving breeding efficiency and accuracy. This study comprehensively reviews the application of whole genome prediction technology in plant and animal breeding, with a focus on exploring its role in improving breeding efficiency. Analyzing current genome selection models and methods, exploring the potential application of GS in improving important agronomic and economic traits, as well as its prospects in different fields. Research has shown that GS technology has greatly improved selection efficiency in multiple breeding projects, particularly in enhancing plant disease resistance and increasing crop yield. In animal breeding, genome selection has been widely applied to improve the reproductive traits, health, and productivity of livestock.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it