Genetic analysis of grain shape and weight after cutting rice husk
Bibliographic record
Abstract
Grain shape and weight are the most important components of rice yield and are controlled by quantitative trait loci (QTLs). In this study, a double-haploid population, derived from the cross of japonica CJ06 and indica TN1, was used to analyze QTLs for grain shape and weight under two conditions: normal growth with unbroken husk and removing partial husk after flowering. Correlation analysis revealed that these traits, except grain weight, had a connection between the two conditions. Twenty-nine QTLs for grain shape and weight were detected on chromosomes 1 to 3; 6; 8 to 10; and 12, with the likelihood of odds value ranging from 2.38 to 5.36, including 10 different intervals. Some intervals were specifically detected after removing partial husk. The results contribute to the understanding of the genetic basis of grain filling and growth regulation, and provide us some assistance for improving grain plumpness in rice breeding.
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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".