AFLP markers for analysis of genetic diversity and structure of teak (<i>Tectona grandis</i>) in India
Bibliographic record
Abstract
Five amplified fragment length polymorphism (AFLP) primer combinations (E-AAC × M-CAT, E-AAC × M-CTG, E-ACA × M-CTC, E-ACA × M-CTA, and E-ACC × M-CTA) were employed for analysis of genetic diversity, differentiation, and structure of 96 genotypes of teak (Tectona grandis L. f.) from 10 natural locations in India. The analysis of the AFLP marker data by both versions, i.e., G ST and θ, of F statistics showed a similar trend due to the outcrossing nature of teak. The primer combination E-AAC × M-CAT detected maximum polymorphism in the teak genome. The analysis of molecular variance assigned a large proportion of the genetic diversity to within sampled locations and a very small proportion to among locations. The population genetic structure resolved by the neighbor joining tree, principal coordinate analysis, and no-admixture and admixture model Bayesian-based analyses irrefutably revealed two distinct centers of teak diversity, i.e., central India and peninsular India. Furthermore, the very high proportion of genetic diversity residing within locations encourages the intensive selection and (or) collection of diverse superior genotypes (elite trees) from each location for the conservation of germplasm and the genetic improvement of teak.
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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".