QTL mapping for stay-green in maize (<i>Zea mays</i>)
Bibliographic record
Abstract
Wang, A.-y., Li, Y. and Zhang, C.-q. 2012. QTL mapping for stay-green in maize ( Zea mays ). Can. J. Plant Sci. 92: 249–256. Stay-green is a desirable character for crop production. In order to explore the genetic basis for stay-green traits in maize, 112 polymorphic simple sequence repeat (SSR) markers were used to analyze 189 F 2 individuals derived from a single cross of inbred lines A150-3-2 (a stay-green inbred line) and Mo17 (a normal inbred line). A total of 14 quantitative trait loci (QTLs) were detected for three stay-green related traits, green leaf area per plant at 30 d after flowering (GLA2), green leaf area per plant at the grain-ripening stage (GLA3), and left green leaf number per plant at the grain-ripening stage (LLN). Single QTL explained from 3.16 to 12.50% of the phenotypic variance. Among them, three were major QTLs. In addition, we analyzed the other two traits, green leaf area per plant in the whole growing period (GLA1) and total leaf number per plant in the whole growing period (TLN), and detected eight QTLs for them. Our results will be helpful to the maize breeders for marker-assisted selection.
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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 | 0.001 | 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".