A model for incorporating novel alleles from the primary gene pool into elite crop breeding programs while reselecting major genes for domestication or adaptation
Bibliographic record
Abstract
Cost, time, linkage drag, and genetic drift work against the incorporation of potentially valuable alleles from exotic or non-adapted germplasm into elite crop plants, particularly for quantitative traits. We present a model, motivated by narrow-leafed lupin (Lupinus angustifolius), for efficient incorporation of new alleles from exotic or non-adapted donors into elite gene pools during two phases of breeding. In Phase 1, probability functions from the binomial distribution provide at least 95% confidence that a potentially valuable donor allele (A') will survive two cycles of backcrossing to elite lines and is fixed in BC2-derived lines. During backcrossing, up to 6 major domestication or adaptation genes from the elite parents are reselected and made homozygous in BC2S0 : 1 family rows. Each plant in the BC2S0 : 1 contains on average 12.5% donor alleles, with >95% probability that a particular donor allele is homozygous in at least one fully domesticated plant in the BC2S0 : 1 population. Plants in these rows or subsequent field trials are selected for valuable quantitative traits, and crossed into elite germplasm to commence Phase 2. Phase 1 is rapid and relatively low cost, and provides a continuous flow of novel genetic diversity into the elite breeding pool.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".