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Record W2808544307 · doi:10.7717/peerj.5007

Evaluating the utility of the female-specific mitochondrial <i>f-orf</i> gene for population genetic, phylogeographic and systematic studies in freshwater mussels (Bivalvia: Unionida)

2018· article· en· W2808544307 on OpenAlexaff
Brent M. Robicheau, Emily E. Chase, Walter R. Hoeh, John L. Harris, Donald T. Stewart, Sophie Breton

Bibliographic record

VenuePeerJ · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversité de MontréalAcadia UniversityDalhousie University
Fundersnot available
KeywordsBiologyMitochondrial DNAGeneticsPhylogeographyPopulationGeneFreshwater bivalveEvolutionary biologyPopulation geneticsZoologyPhylogeneticsBivalviaMollusca

Abstract

fetched live from OpenAlex

Freshwater mussels (order: Unionida) represent one of the most critically imperilled groups of animals; consequently, there exists a need to establish a variety of molecular markers for population genetics and systematic studies in this group. Recently, two novel mitochondrial protein-coding genes were described in unionoids with doubly uniparental inheritance of mtDNA. These genes are the f-orf in female-transmitted mtDNA and the m-orf in male-transmitted mtDNA. In this study, whole F-type mitochondrial genome sequences of two morphologically similar Lampsilis spp. were compared to identify the most divergent protein-coding regions, including the f-orf gene, and evaluate its utility for population genetic and phylogeographic studies in the subfamily Ambleminae. We also tested whether the f-orf gene is phylogenetically informative at the species level. Our preliminary results indicated that the f-orf gene could represent a viable molecular marker for population- and species-level studies in freshwater mussels.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.086
GPT teacher head0.336
Teacher spread0.250 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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Same venuePeerJSame topicAquatic Invertebrate Ecology and BehaviorFrench-language works237,207