Quantitative Trait Locus Analysis for Grain Size Related Traits of Rice
Bibliographic record
Abstract
Grain shape and size, which are characterized by grain length, grain width, grain thickness, and length to width ratio, are the key determinants of grain weight. In this study, a 188-individual F 2 population, which derived from a cross between large grain Indica cultivar Nangyangzhan and medium grain Indica cultivar Ce253, were used to analyze grain size related QTLs with 110 molecular markers. Inclusive Composite Interval Mapping (ICIM), was applied to genome-wide detection of QTLs underlying grain size related traits. For grain length, 4 QTLs were detected on chromosome 1, 3, 4, and 9, respectively. qGL-3 , a major effect QTL, explained 27.60% phenotypic variation of grain length. Four QTLs for grain width were detected on chromosome 2, 3, and 5. Among them, qGW-5a was major QTL for grain width, which explained 21.30% of phenotypic variation. Five QTLs for length to width ratio, which were distributed on chromosome 2, 3, 5, and 12, were identified. The range of their phenotypic contribution varied from 8.60% to 16.86%. For TGW QTLs, only two were identified on chromosome 5 and 6, respectively, with which 13.26% and 9.04% phenotypic contribution. In our results, qGL-4 and qGL-9 for grain length were novel loci. They have not seen reported in literature previously. Our results shed new light on further fine mapping and understanding the molecular genetic mechanism of novel grain size QTLs.
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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 | 0.000 | 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) | 0.001 | 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".