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Record W4248605518 · doi:10.1177/117693430600200022

CoMET: A Mesquite Package for Comparing Models of Continuous Character Evolution on Phylogenies

2006· article· en· W4248605518 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

VenueEvolutionary Bioinformatics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCometCharacter (mathematics)Computer scienceRange (aeronautics)SoftwareBiologyAstrobiologyMathematicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Continuously varying traits such as body size or gene expression level evolve during the history of species or gene lineages. To test hypotheses about the evolution of such traits, the maximum likelihood (ML) method is often used. Here we introduce CoMET (Continuous-character Model Evaluation and Testing), which is module for Mesquite that automates likelihood computations for nine different models offrait evolution. Due to its few restrictions on input data, CoMET is applicable to testing a wide range of character evolution hypotheses. The CoMET homepage, which links to freely available software and more detailed usage instructions, is located at http://www.lifesci.ucsb.edu/eemb/labs/oakley/software/comet.htm .

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.702

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 head0.219
Teacher spread0.205 · 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