Resistance Breeding for Northern Corn Leaf Blight with Dominant Genes, Polygene, and Their Combinations—Effects on Disease Traits
Bibliographic record
Abstract
Resistance breeding is the most effective method to control northern corn leaf blight (NCLB). The objectives were to (1) Assess effects of dominant genes (Ht(s)), polygene (PG), and their combinations to disease rating (DR), number of lesions per leaf (NLPL), and lesion size (LS); (2) Estimate genetic components, general combining abilities (GCA), and heritability under two Line × Tester analyses; and (3) Determine gene action through mid-parent heterosis (MPH) and better-parent heterobeltiosis (BPH) analysis. A total of 163 genotypes, including 120 crosses, their parents, and 10 hybrid checks, were evaluated under two NCLB artificial inoculations in 2015 and 2016. The results indicated that PG had the best resistance to DR, NLPL, and LS in crosses, followed by PGHt(s) and single Ht(s). Ht1 had both resistant and susceptible lesions. Ht2 and Ht3 expressed more resistance to LS significantly, while Htm1 and Htn1 had more resistance to NLPL. Htm1/Ht2, PG/Htm1, and the other 11 combinations were found excellent for NCLB resistance. Line × Tester analysis showed that additive effects were more important, and GCA of Ht(s) reduced disease traits. However, lower narrow sense heritability indicated that additive effects were low. MPH and BPH results showed that dominant and over-dominant gene actions existed for DR and NLPL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".