Detection of Genetic Components of Variation for Yield, Fibre and Quality Traits in Flax (Linum usitatissimum L.)
Bibliographic record
Abstract
The research was carried out using generation mean analysis derived from crosses of TL-11 x Him Alsi-2, Turkey x Binwa and TL-43 x Binwa at two locations i.e. E1 and E2. Out of the three crosses, TL -11 x Him Alsi-2 was the best cross in both the locations. This cross invariably has given 2-3 times higher yield than the remaining crosses. Therefore, the results obtained in the present investigation with respect to the above best cross for generation mean analysis suggested that both additive and dominance gene effects were important for most of the traits but dominance was predominant as compared to additive effects. Duplicate type of gene action was observed in the expression of days to 50 per cent flowering, seed yield per plant, plant height, technical height and fibre traits in E2 for the above cross which implies the use of biparental approach and selection to be deferred to later generations. Complimentary type of gene action was observed for biological yield per plant, seed yield per plant which implies the use of biparental approach and early generation selection. Therefore, it appears worthwhile to go for biparental matings or few cycles of recurrent selection for the accumulation of favourable genes for seed yield and its components in linseed. Different breeding strategy for crosswise as well as location wise is suggested because gene action was different in two locations.
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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 | 0.001 | 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".