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Record W1979548455 · doi:10.1038/srep02901

Wide-ranging barcoding aids discovery of one-third increase of species richness in presumably well-investigated moths

2013· article· en· W1979548455 on OpenAlexfundno aff
Marko Mutanen, Lauri Kaila, J. Tabell

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
FundersMinistero dello Sviluppo EconomicoKoneen SäätiöGovernment of CanadaSuomen KulttuurirahastoGenome CanadaOntario GenomicsOntario Ministry of Economic Development and InnovationOntario Genomics Institute
KeywordsDNA barcodingBiologySpecies richnessBarcodeBiodiversitySpecies complexEvolutionary biologyLepidoptera genitaliaTaxonomic rankZoologyEcologyPhylogenetic treeTaxonGeneticsGene

Abstract

fetched live from OpenAlex

Rapid development of broad regional and international DNA barcode libraries have brought new insights into the species diversity of many areas and groups. Many new species, even within well-investigated species groups, have been discovered based initially on differences in DNA barcodes. We barcoded 437 collection specimens belonging to 40 pre-identified Palearctic species of the Elachista bifasciella group of moths (Lepidoptera, Elachistidae). Although the study group has been a subject of several careful morphological taxonomic examinations, an unexpectedly high number of previously undetected putative species is revealed, resulting in a 34% rise in species number in the study area. The validity of putative new species was subsequently supported with diagnostic morphological traits. We show that DNA barcodes provide a powerful method of detecting potential new species even in taxonomic groups and geographic areas that have previously been under considerable morphological taxonomic scrutiny.

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 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: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 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

Citations56
Published2013
Admission routes1
Has abstractyes

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