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Record W2141671597 · doi:10.1371/journal.pone.0014280

Towards a Global Barcode Library for Lymantria (Lepidoptera: Lymantriinae) Tussock Moths of Biosecurity Concern

2010· article· en· W2141671597 on OpenAlexafffundabout
Jeremy R deWaard, Andrew Mitchell, Melody A. Keena, David Gopurenko, Laura M. Boykin, Karen Armstrong, Michael G. Pogue, João Paulo Matos Santos Lima, Robin Floyd, Robert Hanner, Leland M. Humble

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsNatural Resources CanadaUniversity of GuelphCanadian Forest ServiceUniversity of British ColumbiaRoyal British Columbia Museum
FundersNatural Sciences and Engineering Research Council of CanadaOntario Genomics InstituteOntario GenomicsGenome CanadaCanadian Food Inspection AgencySmithsonian InstitutionTertiary Education CommissionU.S. Department of Agriculture
KeywordsLymantria disparSubspeciesDNA barcodingBiologyBarcodeErebidaeLepidoptera genitaliaEcologyEvolutionary biologyZoology

Abstract

fetched live from OpenAlex

BACKGROUND: Detecting and controlling the movements of invasive species, such as insect pests, relies upon rapid and accurate species identification in order to initiate containment procedures by the appropriate authorities. Many species in the tussock moth genus Lymantria are significant forestry pests, including the gypsy moth Lymantria dispar L., and consequently have been a focus for the development of molecular diagnostic tools to assist in identifying species and source populations. In this study we expand the taxonomic and geographic coverage of the DNA barcode reference library, and further test the utility of this diagnostic method, both for species/subspecies assignment and for determination of geographic provenance of populations. METHODOLOGY/PRINCIPAL FINDINGS: Cytochrome oxidase I (COI) barcodes were obtained from 518 individuals and 36 species of Lymantria, including sequences assembled and generated from previous studies, vouchered material in public collections, and intercepted specimens obtained from surveillance programs in Canada. A maximum likelihood tree was constructed, revealing high bootstrap support for 90% of species clusters. Bayesian species assignment was also tested, and resulted in correct assignment to species and subspecies in all instances. The performance of barcoding was also compared against the commonly employed NB restriction digest system (also based on COI); while the latter is informative for discriminating gypsy moth subspecies, COI barcode sequences provide greater resolution and generality by encompassing a greater number of haplotypes across all Lymantria species, none shared between species. CONCLUSIONS/SIGNIFICANCE: This study demonstrates the efficacy of DNA barcodes for diagnosing species of Lymantria and reinforces the view that the approach is an under-utilized resource with substantial potential for biosecurity and surveillance. Biomonitoring agencies currently employing the NB restriction digest system would gather more information by transitioning to the use of DNA barcoding, a change which could be made relatively seamlessly as the same gene region underlies both protocols.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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 designBench or experimental
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

Citations105
Published2010
Admission routes3
Has abstractyes

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