General and Specific Combining Ability for Quantitative Characters in Sunflower
Bibliographic record
Abstract
Seven sunflower (Helianthus annuus L.) inbred lines were crossed in 7´7 half diallel to obtain 21 F1 hybrids which further used to estimate general combining ability (GCA) and specific combining ability (SCA) effects for yield, head diameter, 1,000-seed weight, plant height and oil content of sunflower. The 7 inbred lines and 21 hybrids were planted in a randomized complete block design with three replications at Nakhon Ratchasima, Thailand during 2008-2009. General combining ability and specific combining ability were estimated for seed yield, head diameter, 1,000-seed weight, plant height and oil content. The results revealed that mean squares for GCA were highly significant for head diameter and significant for yield and oil content. Mean squares for SCA were highly significant for 1,000-seed weight and plant height, while those of yield, head diameter and oil content were non-significant. Components of variance showed that the GCA variance was higher than the SCA variance for yield, head diameter and oil content. These results indicated that additive gene action was more important than non-additive gene action for these traits. Inbred line 5A exhibited the highest GCA effects for yield and oil content, followed by the line 2A. Among all the crosses, 2A´5A showed the greatest positive SCA effects for 1,000-seed weight and oil content. Thus, the two inbred lines (2A and 5A) revealed good potential to be used as parents for hybrid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".