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Record W2620850268 · doi:10.1371/journal.pone.0178548

Probing planetary biodiversity with DNA barcodes: The Noctuoidea of North America

2017· article· en· W2620850268 on OpenAlexafffundabout
Reza Zahiri, J. Donald Lafontaine, Christian Schmidt, Jeremy R deWaard, Evgeny V. Zakharov, Paul D. N. Hebert

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food CanadaCanadian Food Inspection Agency
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCanada First Research Excellence FundOntario GenomicsGenome Canada
KeywordsBarcodeFaunaDNA barcodingBiodiversityGlobal biodiversityBiologyEvolutionary biologyEcologyIntraspecific competitionGeography

Abstract

fetched live from OpenAlex

This study reports the assembly of a DNA barcode reference library for species in the lepidopteran superfamily Noctuoidea from Canada and the USA. Based on the analysis of 69,378 specimens, the library provides coverage for 97.3% of the noctuoid fauna (3565 of 3664 species). In addition to verifying the strong performance of DNA barcodes in the discrimination of these species, the results indicate close congruence between the number of species analyzed (3565) and the number of sequence clusters (3816) recognized by the Barcode Index Number (BIN) system. Distributional patterns across 12 North American ecoregions are examined for the 3251 species that have GPS data while BIN analysis is used to quantify overlap between the noctuoid faunas of North America and other zoogeographic regions. This analysis reveals that 90% of North American noctuoids are endemic and that just 7.5% and 1.8% of BINs are shared with the Neotropics and with the Palearctic, respectively. One third (29) of the latter species are recent introductions and, as expected, they possess low intraspecific divergences.

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 score0.512
Threshold uncertainty score0.239

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 head0.027
GPT teacher head0.193
Teacher spread0.166 · 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

Citations61
Published2017
Admission routes3
Has abstractyes

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Same venuePLoS ONESame topicLepidoptera: Biology and TaxonomyFrench-language works237,207