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Record W2137921387 · doi:10.1098/rsbl.2008.0166

A coalescent framework for comparing alternative models of population structure with genetic data: evolution of Celebes toads

2008· article· en· W2137921387 on OpenAlexafffund
Ben J. Evans, Jimmy A. McGuire, Rafe M. Brown, Noviar Andayani, Jatna Supriatna

Bibliographic record

VenueBiology Letters · 2008
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsMcMaster University
FundersMcMaster UniversityNational Science Foundation
KeywordsCoalescent theoryBiologyIntraspecific competitionHabitat fragmentationEvolutionary biologyPopulation fragmentationInterspecific competitionFragmentation (computing)PopulationEcologyPhylogeographyHabitat destructionGenetic divergencePhylogenetic treeMitochondrial DNAHabitatGenetic variationGenetic diversityGene flowGeneticsGene

Abstract

fetched live from OpenAlex

Isolation of populations eventually leads to divergence by genetic drift, but if connectivity varies over time, its impact on diversification may be difficult to discern. Even when the habitat patches of multiple species overlap, differences in their demographic parameters, molecular evolution and stochastic events contribute to differences in the magnitude and distribution of their genetic variation. The Indonesian island of Sulawesi, for example, harbours a suite of endemic species whose intraspecific differentiation or interspecific divergence may have been catalysed by habitat fragmentation. To further test this hypothesis, we have performed phylogenetic and coalescent-based analyses on molecular variation in mitochondrial and nuclear DNA of the Celebes toad (Bufo celebensis). Results support a role for habitat fragmentation that led to a population structure in these toads that closely matches distributions of Sulawesi macaque monkeys. Habitat fragmentation, therefore, may also have affected other groups on this island.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.088
GPT teacher head0.339
Teacher spread0.251 · 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 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

Citations35
Published2008
Admission routes2
Has abstractyes

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