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Record W4405897476 · doi:10.1038/s41598-024-83134-8

Description and phylogenetic implications of a de novo mitochondrial genome of Rhinichthys atratulus (Teleostei: Leuciscidae) from Connecticut

2024· article· en· W4405897476 on OpenAlexaboutno aff
Timothy S. Earley, Naomi Whitlock, Samuel J. Taylor, Antonio Machado‐Allison, Barry Chernoff

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhylogenetic treeBiologyMitochondrial DNANova scotiaTransfer RNAEvolutionary biologyGeneZoologyGeneticsGeographyArchaeologyRNA

Abstract

fetched live from OpenAlex

We present a de novo mitogenome assembly from a specimen of Rhinichthys atratulus, the Eastern Blacknose Dace, collected in the Connecticut River drainage. R. atratulus is a fish species widely distributed across Atlantic slope drainages from Nova Scotia, Canada to the Roanoke River Drainage, Virginia, United States. The assembly has a total length of 16,646 bp; consists of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a 974 bp D-loop; and has a GC content of 45.7%. We performed two phylogenetic analyses using the de novo assembly and 28 additional cyprinoid mitochondrial genomes: with and without the D-loop. The resulting phylogenetic trees showed the same branching patterns. However, inclusion of the D-loop resulted in nodes with equal or greater support. The trees largely match previously published analyses, while offering new insights into relationships among leuciscid genera. Our findings indicate that the inclusion of the D-loop in mitogenome phylogenetic analyses can help improve bootstrap support at otherwise unresolved nodes.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.241
Teacher spread0.228 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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