{"id":"W2949538565","doi":"10.1186/s12859-019-2610-2","title":"BPG: Seamless, automated and interactive visualization of scientific data","year":2019,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Research Institute; University of Toronto; Government of Ontario; Government of Canada; Canadian Institutes of Health Research; National Science Foundation; Ontario Institute for Cancer Research; Center for Translational Molecular Medicine; University of Pennsylvania; Ontario Genomics Institute; Movember Foundation; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Genome Canada; Prostate Cancer Canada","keywords":"Visualization; Computer science; Data science; Data visualization; World Wide Web; Information retrieval; Data mining","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":[0.004903606,0.00009340359,0.0001915178,0.0004131174,0.0001195544,0.0009481068,0.001314879,0.00003468725,0.0001258204],"category_scores_gemma":[0.001806999,0.00006852594,0.00002529873,0.001147659,0.0001525967,0.001469607,0.001788104,0.00004442291,0.0005879602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001457086,"about_ca_system_score_gemma":0.00008587558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001066965,"about_ca_topic_score_gemma":0.0000174213,"domain_scores_codex":[0.9975597,0.00006642888,0.0007465061,0.0003933219,0.001073973,0.0001601306],"domain_scores_gemma":[0.9965407,0.0005490034,0.0004728567,0.002076745,0.0002997418,0.00006091771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001144795,0.0003781794,0.1170533,0.0007437182,0.00009803671,0.000001560598,0.01182375,0.005906436,0.0004465845,0.02192025,0.571613,0.2699008],"study_design_scores_gemma":[0.0002168106,0.00002117594,0.008521204,0.0000397912,0.000007998694,0.000002075129,0.002254044,0.9684744,0.00009686071,0.0003945836,0.01988696,0.00008402595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4983901,0.00006100898,0.4816506,0.00008848448,0.003942671,0.0007497753,0.0005106696,0.0003325104,0.01427418],"genre_scores_gemma":[0.9267358,0.000003392622,0.06903165,0.00007377782,0.00002201903,0.00000134002,0.0007017844,0.000008487997,0.00342179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.962568,"threshold_uncertainty_score":0.9142616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1609603547062053,"score_gpt":0.4181112973681592,"score_spread":0.2571509426619538,"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."}}