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Record W7143777174 · doi:10.24564/0002021292

Molecular identification of Alpinia species native to the Nansei Islands

2024· article· en· W7143777174 on OpenAlexaff
Mari Narusaka, Kiyotaka Nagaki, Ken-Taro Sekine, Yoshihiro Narusaka

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicGinger and Zingiberaceae research
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsAlpiniaDNA barcodingTaxonPhylogenetic treeRibosomal DNAIdentification (biology)RAPD

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.443
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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