Mapping of QTL Associated with Maize Weevil Resistance in Tropical Maize
Bibliographic record
Abstract
ABSTRACT The maize weevil (MW), Sitophilus zeamais (Motsch.), is an important pest of stored maize (Zea mays L.) in tropical areas. Quantitative trait loci (QTL) associated with MW resistance were analyzed. A total of 163 F2 progeny derived from the cross CML290 (susceptible) × Muneng‐8128 C0 HC1‐18‐2‐1‐1 (resistant), were genotyped with 151 molecular markers. Resistance was evaluated using bioassays on replicated F2:3 progenies. Grain damage (GD), grain weight losses (GWL), MW susceptibility index (Dobie index [DI]), and numbers of adult progeny (AP) were evaluated on grain from F2 lines across two environments. Grain hardness and pericarp/grain ratio were evaluated as putative components of resistance. Composite interval mapping was used for QTL analysis. Five QTL dispersed across the genome explained 28% of the phenotypic variance (σp2) and 50% of the genotypic variance (σg2) for GD. Six QTL were detected with effects on GWL and explained 22% of σp2 and 47% of σg2 Seven QTL (27% σp2 and 78% σg2) were identified for DI and three QTL (10% σp2 and 23% σg2) for AP. Both parents contributed resistance alleles. Genetic effects were mainly dominant (GD, GWL, and DI) and additive (AP). Quantitative trait loci × environment interaction was significant for 50% of the QTL. Since the identified QTL accounted for less than a third of σp2, additional mapping populations are required for mapping MW susceptibility.
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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 | 0.001 |
| 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".