{"id":"W1901890693","doi":"10.1109/tvcg.2015.2467325","title":"TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Visualization; Session (web analytics); Data visualization; Stroke (engine); Process (computing); Artificial intelligence; Data science; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003404806,0.0002479067,0.000278464,0.0006703182,0.0001895617,0.0001774274,0.0006751924,0.0001120533,0.000007405787],"category_scores_gemma":[0.00001631427,0.0002532073,0.00005573086,0.001280159,0.00007034907,0.0009533982,0.00005826234,0.000100347,0.000009865876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003890971,"about_ca_system_score_gemma":0.0001352996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003970176,"about_ca_topic_score_gemma":0.00001842442,"domain_scores_codex":[0.9977529,0.0001742169,0.000551498,0.0006219731,0.0006750322,0.0002243534],"domain_scores_gemma":[0.998033,0.00004959053,0.0002070663,0.0007581091,0.0006293579,0.0003228414],"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":[0.0004299693,0.01349333,0.01981158,0.0004720424,0.0008632407,0.00002395485,0.01828764,0.1443688,0.0003953887,0.7461243,0.01709395,0.03863584],"study_design_scores_gemma":[0.001146841,0.0003024065,0.0002675146,0.00006022746,0.00003168796,0.000003598114,0.00005601508,0.9958662,0.0008392133,0.00006451301,0.001088778,0.0002730028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02099853,0.00001455969,0.9777151,0.00002777267,0.0006753759,0.0002734026,0.0001384858,0.0001500654,0.000006755439],"genre_scores_gemma":[0.9660604,0.0000299707,0.03171172,0.001562287,0.00005838497,0.000007182652,0.0004489725,0.00004150978,0.00007955023],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9460033,"threshold_uncertainty_score":0.999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1539039782704774,"score_gpt":0.3629501310491957,"score_spread":0.2090461527787182,"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."}}