QTL Mapping of Resistance to Ear Rot in Maize Based on SNP Markers and Improvement of High-Yield and Disease-Resistance Traits
Bibliographic record
Abstract
Ear rot is an important disease affecting maize production, resulting in serious yield loss and quality decline. Using a set of maize line populations, QTL mapping was performed to identify genomic regions associated with ear rot resistance. This study found several significant QTLS associated with ear rot resistance, some of which overlapped with regions controlling yield traits, suggesting that both resistance and yield could be improved. The SNP markers identified were used in marker-assisted selection (MAS) strategies to accelerate the development of high-yielding and disease-resistant maize varieties. The aim of this study was to use single nucleotide polymorphism (SNP) markers to locate quantitative trait loci (QTL) for maize ear rot resistance, and to improve high yield and disease resistance. These findings provide important genetic insights into ear rot resistance in maize and provide a framework for future breeding efforts aimed at improving maize productivity and disease resistance.
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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 | 0.000 | 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 | 0.000 |
| 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".