{"id":"W3041248822","doi":"10.1093/bioinformatics/btaa588","title":"TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Energy Research Scientific Computing Center; Joint Genome Institute; Compute Canada","keywords":"Python (programming language); Phylogenetic tree; Metagenomics; Computer science; Tree (set theory); Rank (graph theory); Taxonomic rank; Software; Genome; Biology; Data mining; Ecology; Gene; Genetics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008231538,0.0001563592,0.0001248062,0.00001253117,0.0001251762,0.0000373218,0.0001273389,0.00006921779,0.000003084625],"category_scores_gemma":[0.00005760216,0.0001064287,0.00005445213,0.00007059444,0.0001313085,9.397133e-7,0.000126064,0.00006002387,0.00001549966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003483163,"about_ca_system_score_gemma":0.00005053186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002576131,"about_ca_topic_score_gemma":0.000007881975,"domain_scores_codex":[0.9993475,0.00002760302,0.0002039547,0.0001448305,0.00008860785,0.0001875077],"domain_scores_gemma":[0.9995503,0.00002187666,0.00008830237,0.0001939852,0.00005951612,0.00008596007],"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":[0.0005408702,0.0001052983,0.01120165,0.0003304805,0.0006821057,0.000009910776,0.007125412,0.002215524,0.8641919,0.0006878535,0.01877221,0.0941368],"study_design_scores_gemma":[0.00405453,0.002375305,0.08565938,0.00003759502,0.0002756019,0.00003689395,0.005081031,0.123236,0.4976977,0.000106858,0.2799362,0.001502907],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889203,0.0009212413,0.003739682,0.002617855,0.00008847597,0.0004774575,0.00008556296,0.00001017806,0.003139242],"genre_scores_gemma":[0.992521,0.0001764538,0.003475087,0.003561499,0.0001551669,0.00001653305,0.00003061453,0.00001350439,0.00005006951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3664941,"threshold_uncertainty_score":0.4340039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811510835827763,"score_gpt":0.2240769398793476,"score_spread":0.2059618315210699,"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."}}