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Record W2901568210 · doi:10.1098/rstb.2017.0405

Using insect natural history collections to study global change impacts: challenges and opportunities

2018· review· en· W2901568210 on OpenAlexafffund
Heather M. Kharouba, Jayme M. M. Lewthwaite, Rob Guralnick, Jeremy T. Kerr, Mark Vellend

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversité de SherbrookeSimon Fraser UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnthropoceneBiodiversityEcologyBiologyInsectNatural (archaeology)Global changeClimate changeEvolutionary biologyGeography

Abstract

fetched live from OpenAlex

Over the past two decades, natural history collections (NHCs) have played an increasingly prominent role in global change research, but they have still greater potential, especially for the most diverse group of animals on Earth: insects. Here, we review the role of NHCs in advancing our understanding of the ecological and evolutionary responses of insects to recent global changes. Insect NHCs have helped document changes in insects' geographical distributions, phenology, phenotypic and genotypic traits over time periods up to a century. Recent work demonstrates the enormous potential of NHCs data for examining insect responses at multiple temporal, spatial and phylogenetic scales. Moving forward, insect NHCs offer unique opportunities to examine the morphological, chemical and genomic information in each specimen, thus advancing our understanding of the processes underlying species' ecological and evolutionary responses to rapid, widespread global changes.This article is part of the theme issue 'Biological collections for understanding biodiversity in the anthropocene'.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.530
GPT teacher head0.373
Teacher spread0.157 · 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 designOther design
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

Citations115
Published2018
Admission routes2
Has abstractyes

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