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Record W2626255454 · doi:10.11646/zootaxa.4276.3.9

Diagnostic and phylogenetic utility of the first DNA barcode library for longhorn beetles (Coleoptera: Cerambycidae) from the Russian Far East

2017· article· en· W2626255454 on OpenAlexaff
Vasily V. Grebennikov, Eduard Jendek, MAXIM ED. SMIRNOV

Bibliographic record

VenueZootaxa · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsDNA barcodingLonghorn beetleBarcodeBiologyPhylogenetic treeEvolutionary biologyZoologySympatric speciationGeneticsGene

Abstract

fetched live from OpenAlex

One hundred forty two specimens representing 56 species of longhorn beetles (Cerambycidae) from the Russian Far East were sequenced for a 658 bp fragment of the 5' end of the mitochondrial cytochrome oxidase subunit I gene (COI, =DNA barcode). The data are publicly available in an open access online library (dx.doi.org/10.5883/DS-CERRF). All analysed species could be differentiated using the standard online DNA barcode identification tool, except for a group of three sympatric colour-defined Menesia species. Seven Menesia records share the same Barcode Identification Number (=BIN), while the single specimen of M. flavotecta shares the same haplotype as some of M. sulphurata. Excluding the Menesia case, the Barcode Index Numbers (BINs) uniquely correspond to the analysed species, in all but the four specimens of Chlorophorus simillimus where they share two BINs. Although DNA barcoding aims to develop species identification systems, some phylogenetic signal was apparent in the data and in the Maximum Likelihood analysis, all four subfamilies were recovered as monophyletic. Notwithstanding the few detected deviations from the absolute taxonomic/phylogenetic match, DNA barcoding is a powerful identification tool with a capacity to place an undocumented record among its closest relatives.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.796

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
Teacher spread0.194 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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