{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008149027,0.003555502,0.001747374,0.006121235,0.001038109,0.005791904,0.004850657,0.002052526,0.05898784],"category_scores_gemma":[0.02482392,0.001941847,0.002628926,0.004401734,0.001167659,0.004474578,0.006564626,0.005448549,0.0387341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008638605,"about_ca_system_score_gemma":0.002836437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00357707,"about_ca_topic_score_gemma":0.003443791,"domain_scores_codex":[0.995608,0.001319666,0.0003798023,0.0007336858,0.001648903,0.0003099758],"domain_scores_gemma":[0.9902101,0.004526953,0.0008000741,0.002138331,0.001631965,0.000692701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000473378,0.0001137261,0.002441379,0.00225542,0.0003953006,0.0006056569,0.0009735923,0.006396259,0.01303206,0.01476047,0.7633151,0.1952377],"study_design_scores_gemma":[0.0005435195,0.0001354817,0.006207913,0.001090938,0.0001824233,0.001419481,0.0001978842,0.09214258,0.02644127,0.0975221,0.7735805,0.0005358743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001336117,0.0005853574,0.5703738,0.001149915,0.000435586,0.0002615083,0.01928261,0.4033316,0.003243671],"genre_scores_gemma":[0.02079439,0.001445387,0.8293978,0.00121086,0.0003751365,0.001556514,0.03613734,0.1051203,0.003962312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05898784,"threshold_uncertainty_score":0.197334,"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."}}