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Record W2528073999 · doi:10.1139/gen-2015-0172

DNA recovery from microhymenoptera using six non-destructive methodologies with considerations for subsequent preparation of museum slides

2016· article· en· W2528073999 on OpenAlexvenueaboutno aff
Adriana Guzmán-Larralde, Alba P. Suaste‐Dzul, Adrien Gallou, Kenzy I. Peña‐Carrillo

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHymenoptera taxonomy and phylogeny
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsBiologyDNAComputational biologyMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Because of the tiny size of microhymenoptera, successful morphological identification typically requires specific mounting protocols that require time, skills, and experience. Molecular taxonomic identification is an alternative, but many DNA extraction protocols call for maceration of the whole specimen, which is not compatible with preserving museum vouchers. Thus, non-destructive DNA isolation methods are attractive alternatives for obtaining DNA without damaging sample individuals. However, their performance needs to be assessed in microhymenopterans. We evaluated six non-destructive methods: (A) DNeasy® Blood & Tissue Kit; (B) DNeasy® Blood & Tissue Kit, modified; (C) Protocol with CaCl 2 buffer; (D) Protocol with CaCl 2 buffer, modified; (E) HotSHOT; and (F) Direct PCR. The performance of each DNA extraction method was tested across several microhymenopteran species by attempting to amplify the mitochondrial gene COI from insect specimens of varying ages: 1 day, 4 months, 3 years, 12 years, and 23 years. Methods B and D allowed COI amplification in all insects, while methods A, C, and E were successful in DNA amplification from insects up to 12 years old. Method F, the fastest, was useful in insects up to 4 months old. Finally, we adapted permanent slide preparation in Canada balsam for every technique. The results reported allow for combining morphological and molecular methodologies for taxonomic studies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.223

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 head0.081
GPT teacher head0.278
Teacher spread0.197 · 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 designBench or experimental
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

Citations14
Published2016
Admission routes2
Has abstractyes

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