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Record W4386009490 · doi:10.1111/1744-7917.13262

Spatiotemporal dynamics in butterfly hybrid zones

2023· review· en· W4386009490 on OpenAlexaff
Ananda Regina Pereira Martins, N. Warren, W. Owen McMillan, Rowan D. H. Barrett

Bibliographic record

VenueInsect Science · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcGill University
FundersSmithsonian Tropical Research InstituteCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsButterflyBiologyDivergence (linguistics)Context (archaeology)Strengths and weaknessesHybrid zoneEcologyPaleontology

Abstract

fetched live from OpenAlex

Evaluating whether hybrid zones are stable or mobile can provide novel insights for evolution and conservation biology. Butterflies exhibit high sensitivity to environmental changes and represent an important model system for the study of hybrid zone origins and maintenance. Here, we review the literature exploring butterfly hybrid zones, with a special focus on their spatiotemporal dynamics and the potential mechanisms that could lead to their movement or stability. We then compare different lines of evidence used to investigate hybrid zone dynamics and discuss the strengths and weaknesses of each approach. Our goal with this review is to reveal general conditions associated with the stability or mobility of butterfly hybrid zones by synthesizing evidence obtained using different types of data sampled across multiple regions and spatial scales. Finally, we discuss spatiotemporal dynamics in the context of a speciation/divergence continuum, the relevance of hybrid zones for conservation biology, and recommend key topics for future investigation.

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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.192
GPT teacher head0.301
Teacher spread0.108 · 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
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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