QTL Mapping of Tropical Maize Grain Components Associated with Maize Weevil Resistance
Bibliographic record
Abstract
The maize weevil (MW), Sitophilus zeamais (Motsch.), is a globally distributed pest that causes severe losses in stored maize (Zea mays L.) worldwide. A previous study identified quantitative trait loci (QTL) contributing to reduced grain damage and losses by MW in the maize mapping population CML290 × Muneng‐8128C0HC1‐18‐2‐1‐1. This study was conducted to identify QTL associated with the identified biochemical bases of resistance in the maize grain. Eleven traits were analyzed on grain samples from F2 lines across two environments: p‐coumaric acid (p‐CA), cis‐ and trans‐ferulic acid (FA), four isomers of diferulic acids (DiFA), phenolic acid amides (p‐coumaroyl‐feruloyl putrescine [CFP] and diferuloyl putrescine [DFP]), total DiFA and total phenols acids (PhA). Significant variations were observed for most of the traits, while heritabilities ranged from 0.09 to 0.79. Ten QTL were identified for trans‐ and cis‐FA, 8,5′‐DiFA, 8‐O‐4′‐DiFA, 8,5′‐diferulic acid benzofuran form, DFP, and CFP while seven QTL were identified for p‐CA, 5,5′‐DiFA, FA, total DiFAs, and total PhA. These QTL were dispersed across the maize genome and explain between 25 and 47% of the phenotypic variance (σ2p) and 50 to 98% of the genotypic variance (σ2g). Nine regions were common between QTL associated with MW susceptibility and cell wall bound compounds, suggesting a strong genetic association with MW resistance in tropical maize.
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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 | 0.001 | 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) | 0.001 | 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".