INHERITANCE OF MORPHO-ECONOMIC TRAITS AND COMBINING ABILITY ANALYSIS IN INTRASPECIFIC HYBRIDS OF GOSSYPIUM BARBADENSE L.
Bibliographic record
Abstract
In intraspecific F1 diallel hybrids of Gossypium barbadense L., the inheritance study of traits plant height, boll weight, plant productivity, and 1000-seed weight, with combining ability analysis took place in 2020–2022 at the Institute of Genetics and Plant Experimental Biology, Academy of Sciences, Uzbekistan. These polygenic traits’ inheritance showed different ways in the fine-fiber cotton F1 hybrids. The plant height trait’s inheritance with overdominance and intermediate level of the high/low-performance cultivars. The boll weight trait was mainly in negative overdominance with incomplete dominance of the low-performance cultivar. The inheritance of seed cotton yield had the positive overdominance main control, while the 1000-seed weight had negative and positive overdominance. According to combining ability analysis, the highest positive general combining ability effects resulted in fine-fiber cotton cultivars Surkhan-14 (ĝi = 8.71) and Bo'ston (ĝi =1.86) for plant height, Guzor (ĝi = 0.12) for boll weight, in genotypes Marvarid (ĝi = 2.44) and Surkhan-14 (ĝi = 2.95) for plant productivity, and in cultivars Marvarid (ĝi = 2.3) and Guzor (ĝi = 2.8) for 1000-seed weight. The F1 hybrids Guzor × Surkhan-14, Marvarid × Bo'ston, and Bo'ston × Surkhan-14 showed the highest positive and desirable specific combining ability effects for 1000-seed weight and seed cotton yield. Results concluded that fine-fiber cotton cultivars Marvarid, Surkhan-14, and Guzor can benefit as initial breeding material in selecting high-yielding cotton cultivars.
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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 | 0.001 | 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".