{"id":"W2580191062","doi":"10.1093/sysbio/syw105","title":"Phylogenomics from Whole Genome Sequences Using aTRAM","year":2016,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Phylogenomics; Genome; Coalescent theory; Biology; Phylogenetic tree; Computational biology; Gene; Tree (set theory); Phylogenetics; Genomics; Genetics; Evolutionary biology; Clade","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":[0.0002441847,0.0002119159,0.0004051047,0.00003545753,0.00009402433,0.00001633996,0.0003105176,0.0002053179,0.00001502515],"category_scores_gemma":[0.00008957616,0.0001331988,0.0001218211,0.00004186071,0.0001839901,7.52084e-7,0.0002263118,0.0000368709,0.00004497605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002956673,"about_ca_system_score_gemma":0.00006334879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007503018,"about_ca_topic_score_gemma":0.00003833905,"domain_scores_codex":[0.9985842,0.0001828526,0.0004231194,0.0004426964,0.00005417744,0.0003129496],"domain_scores_gemma":[0.9991044,0.00005582075,0.0002082074,0.00048996,0.00007144481,0.00007016174],"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":[0.00001092564,0.00001236268,0.009099216,0.0002611264,0.0001988831,0.000001169132,0.00006236629,0.00002470741,0.9900062,0.0002465027,0.000005660505,0.00007087895],"study_design_scores_gemma":[0.008636533,0.003654369,0.1118908,0.004730304,0.001544688,0.0004317389,0.00333185,0.001077618,0.7532852,0.09077958,0.01416909,0.006468169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886187,0.004322714,0.005788607,0.0001253541,0.0003490241,0.0003939458,0.0002467246,0.000007255414,0.0001476618],"genre_scores_gemma":[0.9968213,0.00011251,0.002437695,0.0001329256,0.0003347649,0.00004134935,0.00002988517,0.00002085355,0.00006866761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2367209,"threshold_uncertainty_score":0.5431688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240217731885108,"score_gpt":0.2525460667467729,"score_spread":0.2285242935582621,"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."}}