Genetic structure and phylogeography of rice landraces in Yunnan, China, revealed by SSR
Bibliographic record
Abstract
Yunnan Province is one of the largest centers of genetic diversity of Oryza sativa L. in China, and in the world. Using a genetically representative core collection of 692 rice landraces from Yunnan, the genetic structure, differentiation, and geographic diversity of this rice germplasm were analyzed. The accessions were classified into different populations, according to the model-based structure analysis. Model-based populations were characterized through the reconstruction of a neighbor-joining tree, principal coordinate analysis, and the estimation of morphologic and SSR variations. Finally, the distribution of genetic diversity and differentiation among districts were studied. Seven model-based populations were identified on the basis of the structure analysis. This classification was partly consistent with Ting's 5-level taxonomic system. Differentiation between 2 rice subspecies is the most apparent, with a clearer differentiation between soil-watery ecotypes in japonica than in indica; however, differentiation among seasonal ecotypes in indica is clearer than in japonica. Cropping system and man-made restricted-growth environments could be considered to be the main forces driving the intraspecific differentiation of cultivated rice. It has been suggested that, because it possesses the highest genetic diversity and all the necessary conditions as a center of genetic diversity, the southwest region of Yunnan, encompassing Simao, Lincang, and Xishuangbanna districts, is the center of genetic diversity of Yunnan rice landraces.
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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 | 0.002 |
| 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 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".