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COI is better than 16S rRNA for DNA barcoding Asiatic salamanders (Amphibia: Caudata: Hynobiidae)

2011· article· en· W2118730946 on OpenAlexafffund
Yun Xia, Haifeng Gu, Rui Peng, Qin Chen, Yuchi Zheng, Robert W. Murphy, Xiaomao Zeng

Bibliographic record

VenueMolecular Ecology Resources · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsRoyal Ontario Museum
FundersChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiologyDNA barcodingIntraspecific competitionMitochondrial DNAEvolutionary biologyInterspecific competitionGenetic divergenceZoology16S ribosomal RNARibosomal RNAGeneGeneticsEcologyGenetic diversity

Abstract

fetched live from OpenAlex

The 5' region of the mitochondrial DNA (mtDNA) gene cytochrome c oxidase I (COI) is the standard marker for DNA barcoding. However, because COI tends to be highly variable in amphibians, sequencing is often challenging. Consequently, another mtDNA gene, 16S rRNA gene, is often advocated for amphibian barcoding. Herein, we directly compare the usefulness of COI and 16S in discriminating species of hynobiid salamanders using 130 individuals. Species identification and classification of these animals, which are endemic to Asia, are often based on morphology only. Analysis of Kimura 2-parameter genetic distances (K2P) documents the mean intraspecific variation for COI and 16S rRNA genes to be 1.4% and 0.3%, respectively. Whereas COI can always identify species, sometimes 16S cannot. Intra- and interspecific genetic divergences occasionally overlap in both markers, thus reducing the value of a barcoding gap to identify genera. Regardless, COI is the better DNA barcoding marker for hynobiids. In addition to the comparison of two potential markers, high levels of intraspecific divergence in COI (>5%) suggest that both Onychodactylus fischeri and Salamandrella keyserlingii might be composites of cryptic species.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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 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

Citations89
Published2011
Admission routes2
Has abstractyes

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