Development of a wide population of chromosome single-segment substitution lines in the genetic background of an elite cultivar of rice (<i>Oryza sativa</i>L.)
Bibliographic record
Abstract
Naturally occurring allelic variations underlying complex traits are useful resources for the functional analysis of plant genes. To facilitate the genetic analysis of complex traits and the use of marker-assisted breeding in rice, we developed a wide population consisting of 217 chromosome single-segment substitution lines (SSSLs) using Oryza sativa L. 'Hua-Jing-Xian74' (HJX74), an elite Indica cultivar, as recipient, and 6 other accessions, including 2 Indica and 4 Japonica, as donors. Each SSSL contains a single substituted chromosome segment derived from 1 of the 6 donors in the genetic background of HJX74. The total size of the substituted segments in the SSSL population was 4695.0 cM, which was 3.1 times that of rice genome. To evaluate the potential application of these SSSLs for quantitative trait loci detection, phenotypic variations of the quantitative traits of days to heading and grain length in the population consisting of 210 SSSLs were observed under natural environmental conditions. The results demonstrated that there was a wide range of phenotypic variation in the traits in the SSSL population. These genetic materials will be powerful tools to dissect complex traits into a set of monogenic loci and to assign phenotypic values to different alleles at the locus of interest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".