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Record W3154693988 · doi:10.1139/gen-2020-0192

A complement to DNA barcoding reference library for identification of fish from the Northeast Pacific

2021· article· en· W3154693988 on OpenAlexvenueno aff
Sergei V. Turanov, Yuri Ph. Kartavtsev

Bibliographic record

VenueGenome · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
FundersRussian Academy of SciencesMinistry of Science and Higher Education of the Russian Federation
KeywordsDNA barcodingBiologyTaxonSpecies complexGenusFaunaZoologyEvolutionary biologyBarcodeGenetic divergenceEcologyGenetic diversityPhylogenetic treeGeneGeneticsPopulation

Abstract

fetched live from OpenAlex

The seas of the North Pacific Ocean are characterized by a large variety of fish fauna, including endemic species. Molecular genetic methods, often based on DNA barcoding approaches, have been recently used to determine species boundaries and identify cryptic diversity within these species. This study complements the DNA barcode library of fish from the Northeast Pacific area. A library based on 154 sequences of the mitochondrial COI gene from 44 species was assembled and analyzed. It was found that 39 species (89%) can be unambiguously identified by the clear thresholds forming a barcoding gap. Deviations from the standard 2% threshold value resulted in detection of the species Enophrys lucasi in the sample, which is not typical for the eastern part of the Bering Sea. This barcoding gap also made it possible to identify naturally occurring low values of interspecific divergence of eulittoral taxa Aspidophoroides and the deep-sea genus Coryphaenoides. Synonymy of the genus Albatrossia in favor of the genus Coryphaenoides is suggested based on both the original and previously published data.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.264

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 head0.041
GPT teacher head0.272
Teacher spread0.231 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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