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Record W2419226475 · doi:10.1007/978-1-61779-591-6_7

DNA Barcoding Birds: From Field Collection to Data Analysis

2012· article· en· W2419226475 on OpenAlexaff
Darío A. Lijtmaer, Kevin C. R. Kerr, Mark Y. Stoeckle, Pablo L. Tubaro

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsDNA barcodingBiologyBarcodeMitochondrial DNAPrimer (cosmetics)DNA sequencingPseudogeneEvolutionary biologyComputational biologyDNAGeneticsGeneGenomeComputer science

Abstract

fetched live from OpenAlex

As of February 2011, COI DNA barcode sequences (a 648-bp segment of the 5' end of the mitochondrial gene cytochrome c oxidase I, the standard DNA barcode for animals) have been collected from over 23,000 avian specimens representing 3,800 species, more than one-third of the world's avifauna. Here, we detail the methodology for obtaining DNA barcodes from birds, covering the entire process from field collection to data analysis. We emphasize key aspects of the process and describe in more detail those that are particularly relevant in the case of birds. We provide elemental information about collection of specimens, detailed protocols for DNA extraction and PCR, and basic aspects of sequencing methodology. In particular, we highlight the primer pairs and thermal cycling profiles associated with successful amplification and sequencing from a broad range of avian species. Finally, we succinctly review the methodology for data analysis, including the detection of errors (such as contamination, misidentifications, or amplification of pseudogenes), assessment of species resolution, detection of divergent intraspecific lineages, and identification of unknown specimens.

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)0.020
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.007
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.006

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 head0.073
GPT teacher head0.440
Teacher spread0.366 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations45
Published2012
Admission routes1
Has abstractyes

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