Generating and maintaining diversity at the elite level in crop breedingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”
Bibliographic record
Abstract
Most breeding programs develop elite genotypes that are well adapted to the normal range of environmental conditions in the target production region. These elite lines have similar essential alleles for desirable end use characteristics, agronomics, disease resistance, and adaptation in the target region. The genetic makeup of these elite lines is complex. Intermating among the elite lines will often produce new variability through recombination with minimal risk of introducing new undesirable features, and is the source of most new cultivars. Eventually, this variation will be exhausted and new alleles must be introduced into the elite breeding population. Introducing desirable alleles from exotic germplasm may "pollute" the elite gene pool with undesirable alleles. Exotic germplasm may also disrupt essential allele combinations for adaptation, quality, and agronomic performance. New desirable alleles from exotic germplasm can be introgressed into an elite population in a systematic way through limited backcrossing with a minimal disturbance to the finely tuned elite background. Combining recurrent selection within elite germplasm with a systematic introgression from exotic germplasm in the recurrent introgressive population enrichment (RIPE) system has created an open-ended, continually improving, and sustainable elite population breeding system, which is simple, effective, and a regular source of new cultivars.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".