Exploiting Yield Potential in Cucumber (<i>Cucumis sativus</i> L.) through Heterosis Breeding
Bibliographic record
Abstract
Eight genetically divergent parental lines of cucumber were crossed in a diallel pattern to investigate general, specific combining ability and extent of heterosis for yield and its attributing traits. The combining ability analysis revealed that both gca and sca variance were significant for all the characters except equatorial diameter of fruit. Non-additive gene action played a major role in controlling the characters like days taken to first fruit harvest, number of fruits per vine, average fruit weight, diameter of fruit, and average fruit yield. On the basis of gca parent ACC-8 for diameter of fruit and average fruit yield, ACC-2 for days to first fruit harvest and number of fruits per vine, and ACC-4 for average fruit weight were found to be the best general combiner. Cross combinations ACC-2×ACC-6 for days taken to first fruit harvest; ACC-4×ACC-7 for number of fruits per vine; ACC-3×ACC-8 for average fruit weight; ACC-3×ACC-4 for diameter of fruit; ACC-1×ACC-4 for average fruit yield manifested highest sca effects. Cross combination of ACC-1 X ACC-4 and ACC-2 x ACC-6 showed 39.25 and 32.23 heterosis for average fruit yield over standard check, respectively.
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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".