Genetic diversity in cultivars and landraces of <i>Oryza sativa</i> subsp. <i>indica</i> as revealed by AFLP markers
Bibliographic record
Abstract
Genetic diversity among 49 Indian accessions of rice (Oryza sativa subsp. indica), including 29 landraces from Jeypore, 12 modern cultivars, and 8 traditional cultivars from Tamil Nadu, was investigated using AFLP markers. In total, nine primer combinations revealed 664 AFLPs, 408 of which were found to be polymorphic. The percentage of polymorphic AFLPs was approximately the same within the cultivars and landraces. Similar results were obtained when genetic diversity values were estimated using the Shannon-Weiner index of diversity. Genetic diversity was slightly higher in the modern cultivars than in the traditional cultivars from Tamil Nadu. Among the landraces from Jeypore, the lowland landraces showed the highest diversity. The present study showed that the process of breeding modern cultivars did not appear to cause significant genetic erosion in rice. Cluster analysis and the first component of principle component analysis (PCA) both showed a clear demarcation between the cultivars and landraces as separate groups, although the genetic distance between them was narrow. The modern cultivars were positioned between the landraces from Jeypore and the traditional cultivars from Tamil Nadu. The second component of PCA further separated medium and upland landraces from lowland landraces, with the lowland landraces found closest to the traditional and modern cultivars.
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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) | 0.000 | 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".