{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01505153,0.002180019,0.003175057,0.004102017,0.002409078,0.006516242,0.007207187,0.006582936,0.01967901],"category_scores_gemma":[0.06638936,0.002315033,0.002982744,0.005464432,0.004329226,0.009278238,0.003253215,0.007618935,0.005386916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005180513,"about_ca_system_score_gemma":0.003015084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01214585,"about_ca_topic_score_gemma":0.01315716,"domain_scores_codex":[0.9917566,0.005325626,0.0003828068,0.001242935,0.0009607314,0.0003314286],"domain_scores_gemma":[0.9609675,0.03360573,0.001593198,0.00170534,0.001618468,0.0005097238],"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":[0.00004818919,0.00002976657,0.00100123,0.0002571197,0.0001082637,0.000104853,0.0003091088,0.2406805,0.0001321623,0.7229531,0.008148275,0.02622741],"study_design_scores_gemma":[0.00002274393,0.000006645269,0.00013072,0.00005366888,0.00001460775,0.0000534469,0.00002749931,0.3023244,0.00003076535,0.6919434,0.005369643,0.00002245819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002224723,0.001437798,0.9888804,0.001505778,0.0001232646,0.0001197992,0.001233721,0.0005801521,0.00389428],"genre_scores_gemma":[0.191651,0.005906376,0.7749746,0.001505399,0.001136863,0.002625251,0.006392994,0.001076305,0.01473125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01967901,"threshold_uncertainty_score":0.07960105,"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."}}