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Identifying Canadian mosquito species through DNA barcodes

2006· article· en· W2052261418 on OpenAlexafffundabout
Alina Cywinska, Fiona F. Hunter, Paul D. N. Hebert

Bibliographic record

VenueMedical and Veterinary Entomology · 2006
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of GuelphBrock University
FundersNatural Sciences and Engineering Research Council of CanadaHealth Canada
KeywordsBiologyDNA barcodingRange (aeronautics)Mitochondrial DNAZoologyBarcodeEcologySpecies complexPhylogenetic treeGenetics

Abstract

fetched live from OpenAlex

A short fragment of mt DNA from the cytochrome c oxidase 1 (CO1) region was used to provide the first CO1 barcodes for 37 species of Canadian mosquitoes (Diptera: Culicidae) from the provinces Ontario and New Brunswick. Sequence variation was analysed in a 617-bp fragment from the 5' end of the CO1 region. Sequences of each mosquito species formed barcode clusters with tight cohesion that were usually clearly distinct from those of allied species. CO1 sequence divergences were, on average, nearly 20 times higher for congeneric species than for members of a species; divergences between congeneric species averaged 10.4% (range 0.2-17.2%), whereas those for conspecific individuals averaged 0.5% (range 0.0-3.9%).

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 score0.061
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.300
Teacher spread0.268 · 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

Citations345
Published2006
Admission routes3
Has abstractyes

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