{"id":"W2152298208","doi":"10.1093/sysbio/syt022","title":"PhyloBayes MPI: Phylogenetic Reconstruction with Infinite Mixtures of Profiles in a Parallel Environment","year":2013,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":936,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Agriculture and Agri-Food Canada; Université de Montréal","funders":"","keywords":"Computer science; Dirichlet process; Phylogenomics; Hierarchical Dirichlet process; Dirichlet distribution; Gibbs sampling; Inference; Representation (politics); Tree (set theory); Phylogenetic tree; Algorithm; Theoretical computer science; Bayesian probability; Latent Dirichlet allocation; Mathematics; Artificial intelligence; Topic model; Combinatorics; Biology; Boundary value problem","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388725,0.0001544729,0.0004838354,0.0001344572,0.00002329249,0.00002290817,0.00036416,0.0001209659,0.00002002259],"category_scores_gemma":[0.00002987906,0.00009709443,0.00004427782,0.0001370669,0.0001180128,0.00008734352,0.00009816771,0.0000913467,0.00001767327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002054749,"about_ca_system_score_gemma":0.00002983197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000649772,"about_ca_topic_score_gemma":0.000006544609,"domain_scores_codex":[0.9983173,0.0005602055,0.0005020757,0.0003137772,0.00008800294,0.0002186398],"domain_scores_gemma":[0.9990082,0.0001349406,0.0002943086,0.0004836112,0.00003376909,0.00004512804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000669699,0.0006301562,0.1262728,0.04511294,0.0005871805,0.00002417109,0.00875141,0.001084294,0.1952862,0.5137557,0.00007509468,0.1083532],"study_design_scores_gemma":[0.002974501,0.001977353,0.05667921,0.009489769,0.000102075,0.0006714131,0.0004839793,0.1199513,0.02279032,0.7834322,0.00001238424,0.001435522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1759017,0.0005762912,0.8220927,0.0001387719,0.0000859822,0.0009506887,0.00000132294,0.00001978404,0.0002328379],"genre_scores_gemma":[0.629857,0.00001147083,0.3698386,0.00003274267,0.00001021605,0.0002354088,6.580859e-7,0.000003672391,0.00001028003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4539554,"threshold_uncertainty_score":0.3959396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132984968193786,"score_gpt":0.22191598189564,"score_spread":0.2105861322137021,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}