Exploration of genetic selection in rice leaf length and width
Bibliographic record
Abstract
Leaf length and width are two of the most important components of rice (Oryza sativa L.) plant architecture and directly contribute to yield. The genetic effects of leaf length and width are controlled by quantitative trait loci (QTLs). In this study, a double haploid (DH) population derived from a cross of O. sativa subsp. japonica cultivar ‘Maybelle’ and subsp. indica cultivar ‘Baiyeqiu’ (‘BYQ’) was used to determine genetic effects on leaf length and width. Analysis of phenotypic effects indicated that all of the detected traits exhibited continuous, transgressive distributions in the DH population. Correlation analysis revealed a strong association between the two adjacent leaves for the same trait. A total of 25 QTLs within 17 genetic intervals distributed on chromosomes 1, 2, 3, 4, 5, 6, 8, and 12 were detected for rice leaf length and width, with likelihood of odds values that ranged from 2.75 to 10.62. Among these loci, two major QTLs, which were located in the intervals RM3262–RM3452 and RM16432–RM7472, played very important roles in regulating leaf length and width, respectively. Diversity analysis of the two major QTLs revealed that the genetic interval RM16432–RM7472 should be an optimal target for rice breeding programs, and may inspire new research efforts.
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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".