Analysis of phenotypic and genetic variations among populations of <i>Oryza malampuzhaensis</i> show evidence of altitude-dependent genetic changes
Bibliographic record
Abstract
Oryza malampuzhaensis Krish. et Chand., one of the tetraploid taxa in the genus Oryza (Poaceae), is geographically restricted to Western Ghats, southern India. This is one of the poorly understood taxa in the genus, and not much is known about the nature and distribution of its genetic diversity. Five individuals each were selected randomly from 11 populations of O. malampuzhaensis from different altitudinal habitats and were grown in a common-garden experiment for 3 years (1994-1997). Sixty morphological traits and 87 random amplified polymorphic DNA (RAPD) markers, generated by 14 random primers, were used to study the genetic variation among the populations. Elevation-dependent phenotypic variation was observed for a suite of metric traits. A scatterplot of mean values for these traits separated the populations from low, middle, and high altitudes into distinct groups. Cluster analysis using RAPD distance grouped the populations according to their altitudinal habitat, and a similar pattern of clustering was observed with respect to morphological distance also. The mean of both RAPD- and morphology-based pairwise genetic distance of populations belonging to similar altitudinal levels differed significantly. These estimates also depicted a significant decrease in genetic distance with increasing altitude. The results demonstrate that (i) effective isolation from gene flow coupled with natural selection governs genetic structure in O. malampuzhaensis and (ii) ecological heterogeneity associated with elevational gradient has a crucial role in the evolution of O. malampuzhaensis.Key words: Oryza malampuzhaensis, altitude, RAPD, morphological traits, genetic variations, molecular ecology.
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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 | 0.000 | 0.000 |
| 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".