Effect of moisture regimes on combining ability variations of seedling traits in sunflower (<i>Helianthus annuus</i> L.)
Bibliographic record
Abstract
Seedlings traits provide a reliable and rapid technique for evaluating large numbers of genotypes for abiotic stresses. Experiments on sunflower were carried out under two moisture regimes in controlled conditions to study their modifying effect on phenotypic expression and combining ability of seedling traits such as root length (RL), shoot length (SL), root weight (RW), shoot weight (SW), root-to-shoot ratio (R:S), lateral root number (NLR), lateral root density (LRD), wilting rate index and recovery percent (R%), and their genotypic correlation with achene yield. Variation among breeding lines for relative decrease in the seedling traits under the moisture stress regime indicated their differences in moisture sensitivity. Genetic variation for all seedling traits was low over environments, but high within environments. Moisture regimes modified phenotype, ranking among parents, and combining ability of seedling traits. Relative contribution of specific combining ability to total variation decreased under the moisture stress regime for all root-based traits, with a corresponding increase in general combining ability due to either female, male or both. The moisture stress regime was favourable for the expression of additive genetic variability. All seedling traits except SL showed significant correlation with achene yield, which also signified their importance for improving achene yield under drought regimes. From the breeding point of view, R% and RW were more useful traits for evaluating genotypes for drought tolerance. Key words: Sunflower, seedling traits, genetic variability, drought tolerance
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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) | 0.000 | 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) | 0.001 | 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".