Two Cycles of Recurrent Selection Lead to Simultaneous Improvement in Black Spot Resistance and Stem Strength in Field Pea
Bibliographic record
Abstract
Susceptibility to black spot [caused by Mycosphaerella pinodes (Berk. and Blox.) Vestergr.] and weak stem strength in field pea (Pisum sativum L.) are major restrictions to yield, seed quality, and ease of harvest. Previous studies have established that additive genetic variance is present for both traits, and that stem strength is strongly associated with a field test of compressed stem thickness at the base of the stem. This study aimed to improve these two complex traits simultaneously by recurrent selection. Heritabilities and responses to selection for resistance to black spot and stem strength were evaluated during two cycles of recurrent selection after intercrossing diverse genotypes. Response to selection was indicated by a reduction of 10% in the mean black spot leaf disease in the F2 of cycle 2 compared with the F2 of cycle 1, when considered as a percentage of the mean of cycle 1 parents in the same trials. Likewise, there was a 15% increase in stem strength, based on the mean compressed stem thickness in the F2 of cycle 2 compared with the F2 of cycle 1. Broad sense heritability for resistance to black spot leaf disease was moderate (H = 0.63 ± 0.35 across cycles 1 and 2) and for compressed stem thickness was also moderate (H = 0.66 ± 0.34). Genetic gains were realized for both traits simultaneously in the first two cycles and were predicted to be rapid over the next five to six cycles based on a random mating quantitative genetics model.
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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 | 0.000 | 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 | 0.000 |
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".