Bibliographic record
Abstract
Growth duration is one of the important agronomic traits in rice.Breeding for growth duration,especially for earliness,is very important to rice production.Yield component traits of F1 was studied by using combining ability analysis.With four hybrid early CMS,ie.D 64 A,Lexiang 101 A,Lexiang 202 A,Zaoxian A as materials.GCA and SCA variance analysis result showed:Lexiang 101 A was superior to Zaoxian A;Zaoxian A was superior to D 64 A;D 64 A was superior to Lexiang 202 A.Hybrid combination of early utilization result showed:Lexiang 202 A was superior to Zaoxian A;Zaoxian A was superior to D 64 A;D 64 A was superior to Lexiang 101 A.Thus,Lexiang 202 A /B had partially dominant and early major genes,and Lexiang 101 A.,ZaoxianA and D 64 A had tallest combining ability of yield traits.The combining ability of 9 indica type parents,including 4 early CMS lines and 5 medium and late-maturing restorer lines was analyzed in 8 economic traits.The results showed that Lexiang 101 A had the best general combining ability(gca) and specific combining ability(sca).Lexiang 202 A had the smallest gca values,but Lexiang 202 A had the biggest gca values in growth duration,Zaoxian A was superior to D 64 A.In terms of the performances of heterosis in the above traits,there were probability to coordinate the contradiction between earliness and high production existed in earliness hybrid rice by cloning and transferring the earliness of Lexiang 202 B to other hybrid parents.Therefore,the earliness gene had a splendid future in rice breeding.
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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".