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Record W1976670550 · doi:10.1111/1755-0998.12331

Influence of killing method on <scp>L</scp>epidoptera <scp>DNA</scp> barcode recovery

2014· article· en· W1976670550 on OpenAlexfundno aff
Sandi Willows‐Munro, M. Corrie Schoeman

Bibliographic record

VenueMolecular Ecology Resources · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
FundersInternational Development Research CentreGovernment of CanadaUniversity of GuelphOntario Genomics InstituteGenome Canada
KeywordsBiologyBarcodeDNAMicrobiologyMolecular biologyGeneticsComputer scienceOperating system

Abstract

fetched live from OpenAlex

The global DNA barcoding initiative has revolutionized the field of biodiversity research. Such large-scale sequencing projects require the collection of large numbers of specimens, which need to be killed and preserved in a way that is both DNA-friendly and which will keep voucher specimens in good condition for later study. Factors such as time since collection, correct storage (exposure to free water and heat) and DNA extraction protocol are known to play a role in the success of downstream molecular applications. Limited data are available on the most efficient, DNA-friendly protocol for killing. In this study, we evaluate the quality of DNA barcode (cytochrome oxidase I) sequences amplified from DNA extracted from specimens collected using three different killing methods (ethyl acetate, cyanide and freezing). Previous studies have suggested that chemicals, such as ethyl acetate and formaldehyde, degraded DNA and as such may not be appropriate for the collection of insects for DNA-based research. All Lepidoptera collected produced DNA barcodes of good quality, and our study found no clear difference in nucleotide signal strength, probability of incorrect base calling and phylogenetic utility among the three different treatment groups. Our findings suggest that ethyl acetate, cyanide and freezing can all be used to collect specimens for DNA analysis.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

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