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DNA barcoding of eight North American coregonine species

2008· article· en· W2128538757 on OpenAlexaff
Ora L. Schlei, Alexis Crête-Lafrenière, Andrew R. Whiteley, Randy J. Brown, Jeffrey B. Olsen, Louis Bernatchez, John K. Wenburg

Bibliographic record

VenueMolecular Ecology Resources · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiologyDNA barcodingCoregonusCytochrome c oxidase subunit IMitochondrial DNAZoologySubfamilyArcticEvolutionary biologyEcologyGeneticsFisheryGeneFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Coregonine fishes have a circumpolar distribution in the Arctic and sub-Arctic Northern Hemisphere. This subfamily of Salmonidae consists of three genera: Prosopium, Stenodus and Coregonus, including over 30 species. Many species overlap spatially and are difficult to distinguish based on morphological characteristics, especially as larvae or juveniles. Here we present a method for rapid and cost-effective species identification for representatives of the three genera based on sequence variation at the mitochondrial cytochrome c oxidase subunit I gene (COI). We examined eight species common to North America with distributional overlap in Alaska. Mean pairwise sequence divergence for all eight species was 7.04% and ranged from 0.46% to 14.23%. This sequence variation was used to develop a genetic assay based on restriction fragment length polymorphism. In a blind test, this assay provided correct species assignment for 48 of 49 individuals representing all eight species. The single incorrect assignment may reflect hybridization between two closely related species. This DNA barcode-based assay promises to aid fishery managers and researchers by providing a cost-effective alternative to large-scale sequence analysis for identification of North American coregonine fishes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.559

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 studies0.0000.001
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 head0.016
GPT teacher head0.232
Teacher spread0.217 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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