Extent and pattern of genetic differentiation within and between phenotypic populations of<i>Leymus chinensis</i>(Poaceae) revealed by AFLP analysis
Bibliographic record
Abstract
The extent and pattern of genetic differentiation between two naturally occurring phenotypes, grey–green leaf (GGL) and yellow–green leaf (YGL), of Leymus chinensis (Trin.) Tzvel., which colonize distinct habitats in the Songnen Prairie in northeast China, were investigated by amplified fragment length polymorphism (AFLP) analysis. Twelve selected AFLP primer pairs amplified 593 reproducible bands, of which 148 (24.96%) were polymorphic among 69 individuals taken from three populations: two natural ones (YGL and GGL1) and one transplanted (GGL2). Cluster analysis based on the AFLP data categorized the plants into distinct groups that are in line with their phenotypes and population origins, thus denoting clear genetic differentiation between the two phenotypes. This, together with their adaptation to contrasting natural habitats, suggests that the two phenotypes probably represent stabilized ecotypes. The grouping was supported by multiple statistical analyses including Mantel’s test, principal coordinate analysis (PCOORDA), and analysis of molecular variance (AMOVA). The GGL phenotype harbors a higher level of within-population genetic diversity than YGL, possibly reflecting selection by habitat heterogeneity. Although GGL2 is largely similar to its original population (GGL1), further diversification since transplantation was evident. Sequence analysis of a subset of phenotype-specific or phenotype-enriched AFLP bands implicated diverse biological functions being involved in ecological adaptation and formation of the two phenotypes.
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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".