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Record W4416418267 · doi:10.1139/gen-2025-0028

Cryptic diversity identified by DNA barcoding reveals the impact of pleistocene climate oscillations on a forest interior spider

2025· article· en· W4416418267 on OpenAlexvenueno aff
Mariana Costa Terra, Antônio D. Brescovit, Ana Lúcia Dias, Matheus Pires Rincão, Renata da Rosa

Bibliographic record

VenueGenome · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpider Taxonomy and Behavior Studies
Canadian institutionsnot available
FundersFundação AraucáriaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDNA barcodingSpecies complexPhylogeographyCoalescent theoryGenetic diversityLineage (genetic)Phylogenetic treeMitochondrial DNAGenetic divergence

Abstract

fetched live from OpenAlex

Quaternary climate oscillations significantly influenced the configuration of the Brazilian Atlantic Forest. In this study, mitochondrial DNA analysis of Enoploctenus cyclothorax (Bertkau, 1880) was conducted to investigate potential cryptic diversity within populations across the state of Paraná, Brazil. Two divergent genetic lineages were identified based on genetic distances, haplotype network, and phylogenetic inference. A phylogeographical break separates the lineages into two geographic regions; one lineage is found exclusively in the eastern region and the other is found mainly in the northern and western regions of the state. The coalescence tree estimates the divergence time between 1.8 million years and 500 000 years ago, period marked by glaciation, suggesting historical forest fragmentation as a potential isolating mechanism. These findings support the hypothesis of independently evolving units within E. cyclothorax, possibly representing cryptic species, though broader sampling and integrative approaches are necessary for taxonomic validation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.331

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 head0.024
GPT teacher head0.289
Teacher spread0.265 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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