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Record W2550678220 · doi:10.1371/journal.pone.0166118

Exploring Canadian Echinoderm Diversity through DNA Barcodes

2016· article· en· W2550678220 on OpenAlexafffundabout
Kara K S Layton, Erin A. Corstorphine, Paul D. N. Hebert

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Nuclear Safety CommissionGovernment of CanadaGenome CanadaOntario GenomicsOntario Genomics Institute
KeywordsEchinodermDNA barcodingBiologyInvertebrateMarine invertebratesBarcodeBiological dispersalCrustaceanTaxonomic rankEcologyEnvironmental DNAEvolutionary biologyZoologyBiodiversityTaxon

Abstract

fetched live from OpenAlex

DNA barcoding has proven an effective tool for species identification in varied groups of marine invertebrates including crustaceans, molluscs, polychaetes and echinoderms. In this study, we further validate its utility by analyzing almost half of the 300 species of Echinodermata known from Canadian waters. COI sequences from 999 specimens were assigned to 145 BINs. In most cases, species discrimination was straightforward due to the large difference (25-fold) between mean intra- (0.48%) and inter- (12.0%) specific divergence. Six species were flagged for further taxonomic investigation because specimens assigned to them fell into two or three discrete sequence clusters. The potential influence of larval dispersal capacity and glacial events on patterns of genetic diversity is discussed for 19 trans-oceanic species. Although additional research is needed to clarify biogeographic patterns and resolve taxonomic questions, this study represents an important step in the assembly of a DNA barcode library for all Canadian echinoderms, a valuable resource for future biosurveillance programs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.999

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)0.0100.002

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 head0.275
GPT teacher head0.221
Teacher spread0.054 · 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; both teacher heads agree on what is shown here.

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

Citations69
Published2016
Admission routes3
Has abstractyes

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