Heterosis and Specific Combining Ability in Sweet Corn and Its Correlation with Genetic Similarity of Inbred Lines
Bibliographic record
Abstract
The heterosis phenomena has been exploited in hybrid maize field production. Theoretically, heterosis was depending on genetic distance of inbred lines. Meanwhile, different from field corn breeding, sweet corn does not have well defined heterotic group. The objective of this study was to determine genetic similarity (GS) of eight selected inbred lines of sweet corn based on morphological traits and its correlation with specific combining ability (SCA) and heterosis. The eight inbred lines were characterized and crossed in full diallel design. Then, the lines, F1 and its reciprocal, were evaluated by using the Randomized Complete Block Design in three replications. The result showed positive heterosis and SCA effect in most of the hybrids which indicated the heterosis effect in the hybrids performance. Based on the morphological traits, the genetic similarity between the inbred lines was 62.2-82.4%. The inbred lines with 70% similarity coefficient were grouped into three. There was significant correlation between the genetic similarity and heterosis in all traits, except in the plant height. Meanwhile, only SCA of days to maturity and TSS showed significant correlation to genetic similarity. This indicated the usefullness of genetic similarity to identify the potential inbred lines for parental 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 | 0.000 |
| 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".