Molecular identification of Alpinia species native to the Nansei Islands
Bibliographic record
Abstract
Alpinia zerumbet and its related taxa (Zingiberaceae), such as Alpinia formosana, Alpinia intermedia, Alpinia uraiensis, and Alpinia sp., are native to the subtropical and tropical regions in East Asia, including the Nansei Islands. Owing to their morphological similarities, it is difficult to distinguish A. zerumbet from the related taxa. Therefore, a reliable DNA characterization method is required for the breeding and utilization of A. zerumbet and related taxa. Herein, several analytical methodologies have been proposed for the identification of Alpinia species. We showed that a set of random amplified polymorphic DNA (RAPD) markers, high-resolution melting (HRM) analysis, and genotyping by random amplicon sequencing-direct (GRAS-Di) are important tools for understanding the genetic diversity and variation of the Alpinia species growing in the Nansei Islands. The RAPD markers effectively distinguished A. zerumbet from the related taxa. Furthermore, the HRM analysis of nuclear ribosomal DNA internal transcribed spacer 1 revealed clear differences between the species. The phylogenetic analysis using GRAS-Di revealed that each Alpinia species formed independent species-specific subclusters on the phylogenetic tree. Using the molecular markers presented in this study, A. zerumbet and related taxa can be easily and accurately distinguished from each other without morphological characterisation.
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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 | 0.000 |
| 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".