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Record W2548722954 · doi:10.1371/journal.pone.0164933

DNA Barcodes for the Northern European Tachinid Flies (Diptera: Tachinidae)

2016· article· en· W2548722954 on OpenAlexfundno aff
Jaakko Pohjoismäki, Jere Kahanpää, Marko Mutanen

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
FundersKoneen SäätiöGovernment of CanadaSuomen KulttuurirahastoGenome CanadaOntario GenomicsAcademy of FinlandOntario Genomics Institute
KeywordsTachinidaeDNA barcodingBiologyBarcodeLepidoptera genitaliaTaxonomy (biology)ParasitoidZoologyEcologySpecies complexErebidaeSystematicsEvolutionary biologyPhylogenetic treeNatural enemies

Abstract

fetched live from OpenAlex

This data release provides COI barcodes for 366 species of parasitic flies (Diptera: Tachinidae), enabling the DNA based identification of the majority of northern European species and a large proportion of Palearctic genera, regardless of the developmental stage. The data will provide a tool for taxonomists and ecologists studying this ecologically important but challenging parasitoid family. A comparison of minimum distances between the nearest neighbors revealed the mean divergence of 5.52% that is approximately the same as observed earlier with comparable sampling in Lepidoptera, but clearly less than in Coleoptera. Full barcode-sharing was observed between 13 species pairs or triplets, equaling to 7.36% of all species. Delimitation based on Barcode Index Number (BIN) system was compared with traditional classification of species and interesting cases of possible species oversplits and cryptic diversity are discussed. Overall, DNA barcodes are effective in separating tachinid species and provide novel insight into the taxonomy of several genera.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.010

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 head0.217
Teacher spread0.189 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicLepidoptera: Biology and TaxonomyFrench-language works237,207