Construction of three half-sib SSR linkage maps derived from overwintering cultivated rice and segregation distortion loci mapping
Bibliographic record
Abstract
Segregation distortion is a common phenomenon that has been observed in genetics and plant breeding; however, the mechanism of segregation distortion is unknown. In the present study, three half-sib F2 populations derived from three japonica overwinter (perennial) rice varieties (W1, W2, and W3) crossed to the indica rice variety Minghui725 (MH725) were developed to construct three half-sib linkage maps. We established linkage map lengths of 2032.8, 2317.4, and 2108.7 cM with average intervals of 20.1, 20.5, and 19.7 cM using 101, 113, and 107 SSR markers in W1/MH725, W2/MH725, and W3/MH725, respectively. Discrepancies in marker order and genetic linkage distance occurred in the three half-sib linkage maps due to segregation distortion. A total of 88 markers exhibited segregation distortion across the three linkage maps at P < 0.01 level, 42 segregation distortion loci (SDLs) were detected across the three half-sib populations and exhibited variable LOD value that ranged from 3.2 (SDL2f) to 30.1 (SDL5d), and 13 of the 42 SDLs were repeatedly located at the same chromosomal regions of the previously published hybrid sterility quantitative trait loci. Data from this study provide an extensive archive for investigating the genetic characteristic of overwintering cultivated rice and the future exploration and innovation of overwintering 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".