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Record W2514437003 · doi:10.1111/evo.13000

Do saline taxa evolve faster? Comparing relative rates of molecular evolution between freshwater and marine eukaryotes

2016· article· en· W2514437003 on OpenAlexafffund
T. Fatima Mitterboeck, Alexander Y. Chen, Omar A. Zaheer, Y. T. Eddie, Sarah J. Adamowicz

Bibliographic record

VenueEvolution · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
KeywordsBiologyTaxonHabitatPhylogenetic treePhylogeneticsEcologyMarine habitatsEvolutionary biologyTaxonomic rankZoologyGeneGenetics

Abstract

fetched live from OpenAlex

The major branches of life diversified in the marine realm, and numerous taxa have since transitioned between marine and freshwaters. Previous studies have demonstrated higher rates of molecular evolution in crustaceans inhabiting continental saline habitats as compared with freshwaters, but it is unclear whether this trend is pervasive or whether it applies to the marine environment. We employ the phylogenetic comparative method to investigate relative molecular evolutionary rates between 148 pairs of marine or continental saline versus freshwater lineages representing disparate eukaryote groups, including bony fish, elasmobranchs, cetaceans, crustaceans, mollusks, annelids, algae, and other eukaryotes, using available protein-coding and noncoding genes. Overall, we observed no consistent pattern in nucleotide substitution rates linked to habitat across all genes and taxa. However, we observed some trends of higher evolutionary rates within protein-coding genes in freshwater taxa-the comparisons mainly involving bony fish-compared with their marine relatives. The results suggest no systematic differences in substitution rate between marine and freshwater organisms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.471

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 studies0.0000.000
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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.

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

Citations23
Published2016
Admission routes2
Has abstractyes

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