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Record W2548431030 · doi:10.2984/70.4.1

Spatial Scale, Genetic Structure, and Speciation of Hawaiian Endemic Yeasts 1

2016· article· en· W2548431030 on OpenAlexaff
Marc‐André Lachance, Julie D. Collens, Xiao Feng Peng, Alison M. Wardlaw, Lucie Bishop, Lily Hou, William T. Starmer

Bibliographic record

VenuePacific Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsWestern University
FundersNational Park Service
KeywordsBiologyGene flowGenetic algorithmGenetic diversityReproductive isolationGenetic divergenceGenetic structureRange (aeronautics)Hybrid zoneEvolutionary biologyAlleleSexual reproductionEcologyGenetic variationGeneticsGenePopulation

Abstract

fetched live from OpenAlex

Two Hawaiian endemic yeast species, Metschnikowia hawaiiensis and Metschnikowia hamakuensis, were examined by means of multilocus characterization. In spite of their narrow range of distribution, restricted to the island of Hawai‘i, both species were found to be polymorphic at several loci. Alleles of different loci were distributed independently within local populations, confirming that sexual reproduction prevails among these facultatively asexual organisms. No alleles were shared across species, confirming their reproductive isolation. Although the sample size for the northern species, M. hamakuensis, is much less (N = 7) than that for its southern relative, M. hawaiiensis (N = 161), their genetic diversity is comparable. Genetic differentiation was detected in M. hawaiiensis populations at both regional (ca. 30–40 km) and local (ca. 1 km) scales. A single isolate recovered in a separate locality exhibited considerable allelic divergence from others, indicating that genetic isolation can occur over relatively short distances and suggesting a first step towards cessation of gene flow, which is required for speciation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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 designObservational
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

Citations9
Published2016
Admission routes1
Has abstractyes

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