{"id":"W2181924451","doi":"10.26443/msurj.v7i1.100","title":"Bayesian Models for Phylogenetic trees","year":2012,"lang":"en","type":"article","venue":"McGill Science Undergraduate Research Journal","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Phylogenetic tree; Python (programming language); Bayesian probability; Tree (set theory); Biological data; Gibbs sampling; Algorithm; Machine learning; Data mining; Artificial intelligence; Mathematics; Biology; Bioinformatics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003756285,0.0001591707,0.0001465434,0.000244879,0.00209571,0.0001248973,0.0006704041,0.00006948244,0.000003901813],"category_scores_gemma":[0.0003101661,0.0001303131,0.0001208843,0.0004219039,0.0007473291,0.00001107982,0.0003159132,0.0002101481,0.000007106107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008742421,"about_ca_system_score_gemma":0.0002392022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001150615,"about_ca_topic_score_gemma":0.00002478717,"domain_scores_codex":[0.9971884,0.000128539,0.0002458168,0.0003602935,0.000703198,0.001373721],"domain_scores_gemma":[0.9983764,0.00005630386,0.00007521514,0.0003173772,0.0006337111,0.000541031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000059262,0.0001637483,0.001889846,0.0000092968,0.0000526947,0.00000133025,0.00006479267,0.001979606,0.9765443,0.008395953,0.00180665,0.009032516],"study_design_scores_gemma":[0.002565037,0.003323821,0.006724514,0.00005512406,0.00005578529,0.0006591014,0.0009478385,0.009759463,0.5588266,0.1950015,0.2208628,0.001218423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500797,0.006402658,0.02444173,0.005285137,0.001069741,0.0009125306,0.00008226901,0.00000955922,0.01171672],"genre_scores_gemma":[0.9929314,0.001615162,0.004254111,0.00009676096,0.0005508301,0.00003571098,0.000002523004,0.000023414,0.0004901507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4177177,"threshold_uncertainty_score":0.9992034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08086199362187464,"score_gpt":0.361242027806067,"score_spread":0.2803800341841923,"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."}}