{"id":"W2468977341","doi":"10.1111/cgf.12909","title":"PhysioEx: Visual Analysis of Physiological Event Streams","year":2016,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Tech University; Hospital for Sick Children","funders":"","keywords":"Computer science; Workflow; Data stream mining; Visualization; STREAMS; Dashboard; Event (particle physics); Data mining; Data visualization; Field (mathematics); Domain (mathematical analysis); Real-time computing; Artificial intelligence; Data science; Database","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":[0.0001582986,0.0001662378,0.0003633353,0.0004873915,0.00007540448,0.00005695094,0.0009491139,0.00007011479,0.00002614361],"category_scores_gemma":[0.00001448295,0.0001057785,0.0003629411,0.002134526,0.0001200207,0.0003226355,0.0005872986,0.00005853263,0.00001898109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001441544,"about_ca_system_score_gemma":0.00002874553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008179456,"about_ca_topic_score_gemma":0.000006860775,"domain_scores_codex":[0.9984694,0.00007649577,0.0003689717,0.0004392361,0.0003433363,0.000302628],"domain_scores_gemma":[0.9987971,0.0001002948,0.0001920616,0.0006381163,0.0001575348,0.0001149302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005944594,0.0005248821,0.008829121,0.00001322552,0.000868401,0.000005915657,0.00008199349,0.0003487715,0.001800447,0.9144181,0.00712466,0.0659785],"study_design_scores_gemma":[0.0003954043,0.000291239,0.03030411,0.00003488584,0.0001342015,0.000001030493,0.000007820368,0.9567077,0.001198099,0.007577136,0.003072963,0.0002754801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03724026,0.00002109333,0.9617674,0.0005410662,0.0001970964,0.00005909416,0.00002800097,0.0001100335,0.00003599708],"genre_scores_gemma":[0.9950868,0.00006140463,0.003840321,0.0008653528,0.00005047031,0.000002949861,0.00003755365,0.000006739612,0.0000483983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.957927,"threshold_uncertainty_score":0.4313524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999936097246021,"score_gpt":0.3007598536649432,"score_spread":0.280760492692483,"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."}}