Gene Action, Combining Ability, Genetic Expression of Heterosis and Characterization of Rice (<i>Oryza sativa</i> L.) Genotypes for Quality Traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Present investigation was conducted to estimate the per se performance, combining ability and heterosis for grain quality traits in rice using line × tester mating design. Analysis of variance with respect to crosses revealed significant differences for all the traits studied. GCA and SCA results revealed the predominance of SCA variance in relation to GCA variance for all the traits. For aroma, twenty one cross combinations were identified having slight to moderate aroma. Among lines, investigation of highest gca effects illustrated that HPR 2858, HPR 2761, HPR 2754, HPR 2668 and HPR 2748 (P) were good general combiners for different quality traits. Among the testers, HPR 2216 exhibited maximum gca effects for protein content, amylose content and gel consistency while Pusa Basmati 1509 was good combiner for gelatinization temperature. Further, cross combinations HPR 2748 (W) × HPR 2216, HPR 2755 × Kasturi, HPR 2761 × Pusa Basmati 1509 and HPR 2748 (P) × Pusa Basmati 1509 were identified as best specific combination for different grain quality traits. Cross combinations HPR 2748 (W) × HPR 2216, HPR 2761 × HPR 2216, HPR 2748 (P) × Pusa Basmati 1509 showed high degree of heterosis over standard check for different quality parameters.
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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 it