Marker assisted selection for S5 neutral allele in inter-subspecific hybridization of rice
Bibliographic record
Abstract
The present level of heterosis in indica hybrids can be improved by exploiting inter-subspecific gene pool. Hybrid sterility in inter-subspecific hybridization is the major bottleneck, however use ofwide compatible varieties (WCVs) carrying neutral alleles helps in producing fertile hybrids. Conventionally WC varieties are identified by tedious test crossing. To identify WC varieties, 325 tropical japonica and 7 indica lines have been genotyped for the presence of S5 n (neutral allele) with the help of S5-MMS (multiplex marker system) which clearly distinguish indica, japonica and S5 neutral allele. Of the 325 lines, 90 tropical japonica and Swarna ( indica ) showed the presence of S5 n . One hundred and fifty F 1 hybrids, indica × japonica (I × J), were evaluated for their hybrid sterility and spikelet fertility percentage ranged from 4 to 97%. The I × J hybrids with S5n showed higher spikelet fertility; however some hybrids without S5 n showed higher spikelet fertility and among them few tropical japonica lines identified to carry indica allele by S5-MMS marker system, therefore this functional marker is a powerful tool for molecular screening of WC varieties. Hybrids with S5 n showed higher sterility indicating existence of other than embryo sac sterility mechanism of I X J crosses, since S5 n overcomes only embryo sac hybrid sterility. In wild rice O.rufipogon accessions the S5 n allelic status was determined by using S5-MMS marker system. To conclude, S5-MMS is a powerful tool PCR based and with low cost, highly efficient as marker system in identifying WC varieties in different mapping populations including wild rice accessions. Whichsaves one year of breeder’s valuable time in inter-subspecific hybridization in rice.
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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".