Stability of Grain Yield Traits and Their Correlation in Hybrid Rice
Bibliographic record
Abstract
Based on the estimates of stability of grain yield and its components in hybrid rice by AMMI model, the relationship among stability of traits and between stability of traits and the value of grain yield components were analyzed, respectively. The results showed that the effects of genotype and environment for every trait were significant at 1% level. Besides effective tiller number, the other traits had genotype by environment interaction, and 1000 grain weight was the most stable component. The coefficient of correlation of stability between grain yield and seed setting rate and between total spikelets per panicle and both 1000 grain weight and seed setting rate were positive and significant at 5% level. The coefficients of correlation between 1000 grain weight and the stability of total spikelets per panicle and between seed setting rate and its stability were negative and significant at 5% level. The comprehensive improvement of grain yield components in hybrid rice of high and stable grain yield was discussed.
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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 | 0.001 |
| 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) | 0.000 | 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 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".