{"id":"W2144029912","doi":"10.1093/sysbio/syr065","title":"Fast Bayesian Choice of Phylogenetic Models: Prospecting Data Augmentation–Based Thermodynamic Integration","year":2011,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University; Agriculture and Agri-Food Canada","funders":"","keywords":"Atmospheric research; Bayesian probability; Marie curie; Library science; Research center; Phylogenetic tree; Agriculture; Prospecting; Bayesian inference; Biodiversity; Center (category theory); Biology; Agricultural economics; Archaeology; Statistics; Geography; Ecology; Mathematics; Computer science; Political science; Engineering; Economics; Genetics; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003529997,0.0001800178,0.0003346392,0.00005649801,0.00006412422,0.000007854336,0.0004700585,0.0001339836,0.000007859214],"category_scores_gemma":[0.0001139445,0.0001435998,0.00006238234,0.0000747516,0.0001109615,0.000001749752,0.0002281117,0.00005293072,0.00000217369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001242831,"about_ca_system_score_gemma":0.00005648498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001191779,"about_ca_topic_score_gemma":0.000151323,"domain_scores_codex":[0.9986128,0.000192871,0.0005166507,0.0004194484,0.00006636938,0.0001919111],"domain_scores_gemma":[0.9986124,0.00003572746,0.0003465787,0.0008526146,0.0001158833,0.0000367623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004138872,0.000114212,0.015359,0.002436677,0.0002695879,4.335114e-7,0.0006831297,0.0003690184,0.978561,0.001589919,0.000007600913,0.0005680589],"study_design_scores_gemma":[0.003813229,0.00267019,0.04924487,0.002551912,0.0008828933,0.00004015424,0.005058351,0.4739888,0.437405,0.02257227,0.00001357508,0.001758787],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8289313,0.001397895,0.1676406,0.00001730402,0.0001716662,0.0008211355,0.00007684912,0.00000738483,0.0009359499],"genre_scores_gemma":[0.9921239,0.00002002818,0.007472685,0.00004634113,0.00006091694,0.00009224372,0.0001496302,0.00001918003,0.00001508677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.541156,"threshold_uncertainty_score":0.5855832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06833684578545228,"score_gpt":0.2860064725691254,"score_spread":0.2176696267836731,"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."}}