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 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 | 0.000 | 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) | 0.000 | 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".