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Record W2057423125 · doi:10.3897/zookeys.16.239

Prospects for using DNA barcoding to identify spiders in species-rich genera

2009· article· en· W2057423125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueZooKeys · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpider Taxonomy and Behavior Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario GenomicsOntario Genomics InstituteGenome CanadaOntario Innovation Trust
KeywordsPolyphylyParaphylyDNA barcodingMonophylyBiologySpecies complexTaxonTaxonomy (biology)Evolutionary biologyGenetic divergenceDivergence (linguistics)ZoologyEcologyPhylogeneticsCladePhylogenetic treeGenetic diversity

Abstract

fetched live from OpenAlex

While previous research has indicated the utility of DNA barcoding in identifying spider species sampled from a localized region, the effectiveness of this method over a broader geographic scale and with denser taxon sampling has not yet been extensively considered. Using both new and published data from 1801 individuals belonging to 361 morphospecies, this study examined intra- and interspecific divergences for 19 genera that were each represented by at least 10 morphospecies. We particularly focused on increasing species-level sampling in order to better characterize levels of interspecific divergence within species-rich genera and to examine the prevalence of a “barcode gap” (discontinuity between intra- and interspecific divergences). Overall, the mean intraspecific divergence value was found to be 2.15%, the average maximum intraspecific divergence was 3.16%, while the mean divergence between nearest interspecific neighbours was 6.77%, demonstrating the typical presence of a barcode gap. Of the 66% of morphospecies that formed monophyletic sequence clusters, the majority (92.5%) possessed a barcode gap. We also examine possible biological explanations for the large proportion of paraphyletic and polyphyletic clusters and discuss the need for further taxonomic investigations. The overlap between intra- and interspecific divergences was not unexpected for some ‘species’, such as Pardosa groenlandica, since prior morphological studies have suggested that it is an example of a species complex. However, other cases of high intraspecific divergences may reflect cryptic species diversity, indicating the need for a taxonomic approach that combines both morphological and molecular methods. The list of the species, COI sequences, and source references used in the analysis is published as a dataset under doi: 10.3897/zookeys.16.239.app.A.ds. The list of analyzed species, mean and maximum intraspecific divergences, distances to the nearest neighbouring species in its genus, general localities, and lifestyle characteristics is published as a dataset under doi: 10.3897/zookeys.16.239.app.B.ds.

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.

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 score0.111
Threshold uncertainty score0.587

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 head0.056
GPT teacher head0.334
Teacher spread0.278 · 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