Combining Ability and Heterosis for Agronomic and Yield Traits in Indica and Japonica Rice Crosses
Bibliographic record
Abstract
Understanding genetic variability and mode of gene action for agronomic and yield related traits is important in formulation of effective rice breeding program for genetic enhancement of grain yield. Combining ability analysis and heterosis was conducted to identify yield associated traits from nine male indicas and three female japonicas, together with their 27 F1 hybrids. Four parental lines, including Basmati 370, Basmati 217, K2-54 and Komboka showed good general combining ability in days to 50% flowering, days to maturity, number of tillers plant-1, number of spikelet’s panicle-1, number of panicles plant-1, number of filled grains panicle-1, and grain yield. While the combine K2-9 × Komboka, K2-9 × Basmati 370, K2-54 × Dourado Precoce and K2-54 × Basmati 217 showed specific good for grain yield. The hybrids K2-9 × Basmati 370, K2-8 × Basmati 217, K2-54 × Basmati 217 and K2-9 × Komboka showed 20% excess in standard check variety, suggesting that they could be good breeding donors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".