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Record W2530409686 · doi:10.1139/gen-2015-0174

Species-level identification of the blowfly <i>Chrysomya megacephala</i> and other Diptera in China by DNA barcoding

2016· article· en· W2530409686 on OpenAlexvenueno aff
Deyi Qiu, Charles E. Cook, Qiaoyun Yue, Jia Hu, Xiaoya Wei, Jian Chen, Dexing Liu, Keqiang Wu

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChrysomya megacephalaBiologyDNA barcodingZoologyBarcodeLarvaEntomologyEcologyCalliphoridae

Abstract

fetched live from OpenAlex

The blowfly Chrysomya megacephala, or oriental latrine fly, is the most common human-associated fly of the oriental and Australasian regions. Chrysomya megacephala is of particular interest for its use in forensic entomology and because it is a disease vector. The larvae are economically important as feed for livestock and in traditional Chinese medicine. Identification of adults is straightforward, but larvae and fragments of adults are difficult to identify. We collected C. megacephala, its allies Chrysomya pinguis and Protophormia terraenovae, as well as flies from 11 other species from 52 locations around China, then sequenced 658 base pairs of the COI barcode region from 645 flies of all 14 species, including 208 C. megacephala, as the basis of a COI barcode library for flies in China. While C. megacephala and its closest relative C. pinguis are closely related (mean K2P divergence of 0.022), these species are completely non-overlapping in their barcode divergences, thus demonstrating the utility of the COI barcode region for the identification of C. megacephala. We combined the 208 C. megacephala sequences from China with 98 others from public databases and show that worldwide COI barcode diversity is low, with 70% of all individuals belonging to one of three haplotypes that differ by one or two substitutions from each other, reflecting recent anthropogenic dispersal from its native range in Eurasia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.126

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.207
Teacher spread0.188 · 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.

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

Citations9
Published2016
Admission routes1
Has abstractyes

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