Genetic Diversity Analysis Reveals that Geographical Environment Plays a More Important Role than Rice Cultivar in Villosiclava virens Population Selection
Bibliographic record
Abstract
Rice false smut caused by Villosiclava virens is an economically important disease of grains worldwide. The genetic diversity of 153 isolates from six fields located in Wuhan (WH), Yichang Wangjia (YCW), Yichang Yaohe (YCY), Huanggang (HG), Yangxin (YX), and Jingzhou (JZ) in Hubei province of China were phylogenetically analyzed to evaluate the influence of environments and rice cultivars on the V. virens populations. Isolates (43) from Wuhan were from two rice cultivars, Wanxian 98 and Huajing 952, while most of the other isolates from fields YCW, YCY, HG, YX, and JZ originated from different rice cultivars with different genetic backgrounds. Genetic diversity of isolates was analyzed using random amplified polymorphic DNA (RAPD) and single-nucleotide polymorphisms (SNP). The isolates from the same cultivars in Wuhan tended to group together, indicating that the cultivars had an important impact on the fungal population. The 110 isolates from individual fields tended to cluster according to geographical origin. The values of Nei's gene diversity (H) and Shannon's information index (I) showed that the genetic diversity among isolates was higher between than within geographical populations. Furthermore, mean genetic distance between groups (0.006) was higher than mean genetic distance within groups (0.0048) according to MEGA 5.2. The pairwise population fixation index (FST) values also showed significant genetic differentiation between most populations. Higher genetic similarity of isolates from individual fields but different rice cultivars suggested that the geographical factor played a more important role in the selection of V. virens isolates than rice cultivars. This information could be used to improve the management strategy for rice false smut by adjusting the cultivation measures, such as controlling fertilizer, water, and planting density, in the rice field to change the microenvironment.
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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 | 0.001 |
| 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".