Utilization of doubled haploid technique in <i>Brassica rapa</i> population improvement
Bibliographic record
Abstract
The utilization of doubled haploid (DH) plants in population improvement of Brassica rapa was studied by randomly intercrossing 4, 8, 12 and 22 DH lines developed from the B. rapa cultivar Reward and the B. rapa breeding line DSC3 for two generations to constitute synthetic populations. The synthetic populations and the DH plants used in their formation were evaluated for agronomic performance at two locations in the field in 1996 and for genetic variation using random amplified polymorphic DNA (RAPD) analysis. Intercrossing as few as four DH lines from the breeding line DSC-3 produced a synthetic population with improved performance over that of the contributing DH lines. The synthetic population produced from the interpollination of eight DH lines showed an agronomic performance over that of the contributing DH lines to a level similar to the Reward donor population. RAPD analysis efficiently characterized the genotypic variation present in DH lines and synthetic populations, detecting 22–72% polymorphism between DH lines, 17–53% and 27–47% polymorphism in the first and second synthetic populations, respectively. This characterization may be useful as a tool in the reestablishment of heterogeneity and recovery of agronomic performance in B. rapa synthetic populations derived from DH lines by determining the level of genetic variability among DH lines and therefore optimal population size. Key words: Brassica rapa, synthetic population, doubled haploids, RAPD, agronomic performance
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".