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Record W1909668108 · doi:10.3732/apps.1500020

Development and evaluation of microsatellite markers for <i>Acer miyabei</i> (Sapindaceae), a threatened maple species in East Asia

2015· article· en· W1909668108 on OpenAlexfundno aff
Ikuyo Saeki, Akira S. Hirao, Tanaka Kenta

Bibliographic record

VenueApplications in Plant Sciences · 2015
Typearticle
Languageen
FieldChemistry
TopicPlant-Derived Bioactive Compounds
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceFujiwara Natural History FoundationUniversity of British Columbia
KeywordsBiologyMicrosatelliteInbreedingGenetic diversityLocus (genetics)Threatened speciesPopulationGenetic markerGene flowEndangered speciesAlleleGenetic variationEvolutionary biologyGeneticsEcologyGeneDemography

Abstract

fetched live from OpenAlex

PREMISE OF THE STUDY: Twelve microsatellite markers were developed and characterized in a threatened maple species, Acer miyabei (Sapindaceae), for use in population genetic analyses. METHODS AND RESULTS: Using Ion Personal Genome Machine (PGM) sequencing, we developed microsatellite markers with perfect di- and trinucleotide repeats. These markers were tested on a total of 44 individuals from two natural populations of A. miyabei subsp. miyabei f. miyabei in Hokkaido Island, Japan. The number of alleles per locus ranged from two to eight. The observed and expected heterozygosities per locus ranged from 0.05 to 0.75 and from 0.05 to 0.79, respectively. Some of the markers were successfully transferred to the closely related species A. campestre, A. platanoides, and A. pictum. CONCLUSIONS: The developed markers will be useful in characterizing the genetic structure and diversity of A. miyabei and will help to understand its spatial genetic variation, levels of inbreeding, and patterns of gene flow, thereby providing a basis for conservation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.304
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2015
Admission routes1
Has abstractyes

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