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Record W3033062148 · doi:10.1139/gen-2019-0226

First records raise questions: DNA barcoding of Odonata in the middle of the Mediterranean

2020· article· en· W3033062148 on OpenAlexaffvenue
Tomasz Rewicz, Arnold Móra, Grzegorz Tończyk, Ada Szymczak, Michał Grabowski, Eman Calleja, Bálint Pernecker, Zoltán Csabai

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingBiologyOdonataBarcodeZoologyCytochrome c oxidase subunit IRange (aeronautics)Mitochondrial DNATaxonomy (biology)EcologyGenetics

Abstract

fetched live from OpenAlex

We present the results of the first-ever DNA barcoding study of odonates from the Maltese Islands. In total, 10 morphologically identified species were collected during a two-week long expedition in 2018. Eighty cytochrome c oxidase subunit I (COI) barcodes were obtained from the collected specimens. Intra- and interspecific distances ranged from 0.00% to 2.24% and 0.48% to 17.62%, respectively. Successful species identification based on ascribing a single morphological species to a single Barcode Index Number (BIN) was achieved for eight species (80%). In the case of two species, Ischnura genei and Anax parthenope, BINs were shared with other closely related species. The taxonomic status of I. genei is questionable and the phylogenetic relationship between A. imperator/parthenope is not clear. Further studies involving a series of adult specimens collected in a wide spatial range and nuclear markers are necessary to resolve these cases. Therefore, this dataset serves as an initial DNA barcode reference library for Maltese odonates, within a larger project: Aquatic Macroinvertebrates DNA Barcode Library of Malta.

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)0.002
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.052
GPT teacher head0.214
Teacher spread0.162 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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