Resistance Breeding to Northern Corn Leaf Blight with Dominant Genes, Polygene, and Their Combinations—Effects to Yield Traits
Bibliographic record
Abstract
Resistance breeding is the most economic method to control northern corn leaf blight (NCLB). The objectives of these studies were: To assess effects of dominant genes (Ht(s)), polygene (PG), and combinations on percent leaf area affected (PLAA), yield, kernel moisture, kernel number per ear, and 100-kernel weight; to understand genetic action of combinations; to predict losses and effects of resistant genes for yield traits with PLAA; and to assess yield under different NCLB epidemic conditions. Two experiments were conducted. E1 had 120 crosses, their parents, and ten hybrid checks, inoculated NCLB twice in 2015 and 2016; and E2 had 85 crosses and 10 hybrids, with none, one, and two inoculation treatments in 2015. E1 results showed the order of PLAA was Ht3 ≈ Ht2 ≈ PGHtm1 ≈ PGHt1< PGHt3 ≈ PGHt2 < PGHtn1 < PG < Ht1 < Htn1 ≈ Htm1. The order of Ht(s) effects for yield was Ht2 > Ht3 > Ht1 > Htm1 > Htn1. Gene effects of cross ≈ gene effects of (female + male) for all five traits. Predicted losses and predicted effects of resistant genes between yield traits with PLAA were determined. E2 results indicated resistant genes increased yield more efficiently under NCLB epidemic environments.
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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) | 0.001 | 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".