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Record W2163605328 · doi:10.1111/mec.12153

Geological events play a larger role than <scp>P</scp>leistocene climatic fluctuations in driving the genetic structure of <i><scp>Q</scp>uasipaa boulengeri</i> (<scp>A</scp>nura: <scp>D</scp>icroglossidae)

2012· article· en· W2163605328 on OpenAlexaff
Yan Fang, Weiwei Zhou, HAI-TAO ZHAO, Zhi-Yong Yuan, Yunyu Wang, Ke Jiang, Jie‐Qiong Jin, Robert W. Murphy, Jing Che, Ya‐Ping Zhang

Bibliographic record

VenueMolecular Ecology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsBiologyGene flowGlacial periodGenetic structureEcologyPopulationEvolutionary biologyZoologyGenetic variationGeneGeneticsPaleontology

Abstract

fetched live from OpenAlex

Paleoclimatic and paleogeological events have been identified as being the two main drivers of genetic structuring in extant organisms. We used a montane stream-dwelling frog, Quasipaa boulengeri, to explore the relative roles played by these drivers on species in southern China, a region needing thorough studies. We detected four major matrilines, and no broadly distributed haplotypes occurred. The complex orogenesis of south-western China drove matrilineal divergence in Q. boulengeri into highly structured geographical units. These matrilines subsequently persisted in situ with stable populations rather than undergoing expansions during glacial cycling. The unification of the upper and middle Yangtze River in the Three Gorges mountain region mediated downstream colonization of this frog. Analyses identified geological events as playing a larger role than climatic fluctuations in driving the population history of Q. boulengeri. Nuclear allele analyses indicated gene flow; this maintained genetic cohesion of the species. South-eastern Sichuan Basin was identified as the area of secondary contact for several matrilines, and this area deserves further study and special protection.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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.

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

Citations126
Published2012
Admission routes1
Has abstractyes

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