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Record W2735593085 · doi:10.1073/pnas.1621504114

Nuclear genomes distinguish cryptic species suggested by their DNA barcodes and ecology

2017· article· en· W2735593085 on OpenAlexaff
Daniel H. Janzen, John M. Burns, Qian Cong, Winnie Hallwachs, Tanya Dapkey, Ramya Manjunath, Mehrdad Hajibabaei, Paul D. N. Hebert, Nick V. Grishin

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsUniversity of Guelph
FundersNational Institute of General Medical SciencesWelch FoundationHoward Hughes Medical Institute
KeywordsBiologySpecies complexParapatric speciationSympatric speciationDNA barcodingEcologyMitochondrial DNAEvolutionary biologyPhylogenetic treeGene flowGenetic variation

Abstract

fetched live from OpenAlex

Significance Thirteen years of mitochondrial DNA barcoding of 15,000+ species of Lepidoptera and their parasitoids living in Area de Conservación Guanacaste, northwestern Costa Rica, indicate several thousand cases where barcodes combined with ecology suggest unrecognized cryptic species, substantially increasing species counts. Here, we show that the slightly different barcodes of three extremely similar parapatric–sympatric species of butterflies covary not only with ecology and subtle morphological traits but also with nuclear genomes—a finding that we predict will be commonplace and a method that we predict will be widely used. The barcodes of the century-old type specimens of Udranomia kikkawai from Venezuela reveal that this name applies to one of the three Costa Rican cryptic species; the others we describe as new.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations160
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicLepidoptera: Biology and TaxonomyFrench-language works237,207