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DNA Barcoding in Mammals

2012· article· en· W92163274 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingBiologyTaxonIdentification (biology)Evolutionary biologyCytochrome c oxidase subunit IScope (computer science)Computational biologyTaxonomic rankBarcodeVertebrateSpecies complexMitochondrial DNAEcologyComputer scienceGeneGeneticsPhylogenetic tree

Abstract

fetched live from OpenAlex

DNA barcoding provides an operational framework for mammalian taxonomic identification and cryptic species discovery. Focused effort to build a reference library of genetic data has resulted in the assembly of over 35 K mammalian cytochrome c oxidase subunit I sequences and outlined the scope of mammal-related barcoding projects. Based on the above experience, this chapter recounts three typical methodological pathways involved in mammalian barcoding: routine methods aimed at assembling the reference sequence library from high quality samples, express approaches used to attain cheap and fast taxonomic identifications for applied purposes, and forensic techniques employed when dealing with degraded material. Most of the methods described are applicable to a range of vertebrate taxa outside Mammalia.

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.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.352
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.047
GPT teacher head0.420
Teacher spread0.374 · 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