Genetic Diversity Among Guangxi Local Maize Varieties and Canadian Maize Populations
Bibliographic record
Abstract
【Objective】 In order to use local varieties and exotics to broaden the genetic base of Guangxi improved maize germplasm, the genetic diversity among 45 Guangxi local maize varieties (OPVs) and 15 Canadian maize populations were analyzed. 【Method】The bulked-SSR strategy was adopted in this study. A total of 240 DNA samples were extracted, which consisted of 4 bulks of DNA from 10 individual plants per bulk to represent each population or OPV (using equal amounts of DNA per plant). 【Result】The results showed that 245 alleles were detected with 70 pairs of selected primers in the 240 bulks from the 60 OPVs or populations. The number of alleles per locus averaged 3.5 and ranged 2-6. The clustering results using the UPGMA method based on the genetic similarities between each pair of populations showed that the 45 Guangxi local OPVs and the 15 Canadian populations were classified into 2 groups, respectively. Each group consisted of both flint and dent subgroups. The waxy maize samples from Guangxi were not clustered as an independent group, but dispersed in the flint group. 【Conclusion】The variation of Guangxi subtropical maize is higher than that of Canadian maize germplasm. The genetic base of Guangxi improved maize germplasm can be broadened with the Guangxi local varieties using the clustering results and diagnostic alleles, which will be of great importance to find useful germplasm in maize breeding efforts.
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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 | 0.001 | 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".