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Record W2901017468 · doi:10.1111/1755-0998.12963

DNA barcodes identify 99 per cent of apoid wasp species (Hymenoptera: Ampulicidae, Crabronidae, Sphecidae) from the Western Palearctic

2018· article· en· W2901017468 on OpenAlexafffund
Christian Schmid‐Egger, Jakub Straka, Toshko Ljubomirov, Gergin Blagoev, Jérôme Morinière, Stefan Schmidt

Bibliographic record

VenueMolecular Ecology Resources · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
FundersBayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und KunstOntario Ministry of Research and InnovationOntario Ministry of Research, Innovation and ScienceBundesministerium für Bildung und ForschungUtah Agricultural Experiment Station
KeywordsSphecidaeBiologyFaunaDNA barcodingZoologyParaphylyHymenopteraEcologyTaxonomy (biology)Phylogenetics

Abstract

fetched live from OpenAlex

The apoid wasps have traditionally been regarded as a paraphyletic assemblage of four families (Ampulicidae, Crabronidae, Heterogynaidae and Sphecidae) that are closely related to the bees (Anthophila). The present study covers the three families of apoid wasps known to occur in Europe, that is, the Ampulicidae, Crabronidae and Sphecidae. DNA barcode sequences of 3,695 specimens of apoid wasps were analysed for the present study, including 21 specimens of Ampulicidae, 3,398 Crabronidae and 276 Sphecidae. The sequences of the dataset represent 661 species of apoid wasps, including two species of Ampulicidae, 613 of Crabronidae and 46 species of Sphecidae. The dataset includes DNA barcodes of 240 species of German apoid wasps, representing 88% of the German fauna, and 578 European species, representing 65% of the European apoid wasp fauna. The study demonstrates that virtually all species of the three examined families can be reliably identified by DNA barcodes. The implications of highly congruent results between traditional taxonomy and DNA barcoding for the reliable application of DNA-based identifications are discussed.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.021
GPT teacher head0.216
Teacher spread0.195 · 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.

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

Citations42
Published2018
Admission routes2
Has abstractyes

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