{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002391988,0.001315965,0.0005898856,0.002917497,0.0003668874,0.002494985,0.0009138836,0.0007084875,0.01121226],"category_scores_gemma":[0.008570246,0.0003808601,0.0005658711,0.00114977,0.0004740567,0.00185454,0.002620006,0.0009800928,0.001005368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002534095,"about_ca_system_score_gemma":0.0006042001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130935,"about_ca_topic_score_gemma":0.0008710328,"domain_scores_codex":[0.9992881,0.0002452487,0.00006092897,0.0001247006,0.0002370175,0.00004396278],"domain_scores_gemma":[0.9951761,0.003165856,0.0004004024,0.0004419854,0.0005342745,0.0002813289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003645917,0.0006277369,0.01869048,0.002754464,0.0005519498,0.002162488,0.006214904,0.05451428,0.1173809,0.01728084,0.07320667,0.7029694],"study_design_scores_gemma":[0.0007824069,0.0009092649,0.03193586,0.0009412615,0.0002648988,0.001607121,0.002086192,0.6826642,0.09926689,0.04837454,0.130728,0.0004394526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05759789,0.0006796185,0.8837913,0.001086856,0.0003223584,0.0004896875,0.005163418,0.04687709,0.003991864],"genre_scores_gemma":[0.4787708,0.0009659088,0.5078479,0.0004159686,0.0002631421,0.000826742,0.00388842,0.003473576,0.003547548],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01121226,"threshold_uncertainty_score":0.03750873,"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."}}