{"id":"W2135909255","doi":"10.1162/leon.2007.40.1.71","title":"The Art and Science of Visualizing Simulated Blood-Flow Dynamics","year":2007,"lang":"en","type":"article","venue":"Leonardo","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Canada Research Chairs; University of Toronto","funders":"","keywords":"Dynamics (music); Context (archaeology); Flow (mathematics); Computer science; Human–computer interaction; Blood flow; Data science; Cognitive science; Mechanics; Sociology; Physics; Psychology; Medicine; Biology","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":[0.0004523524,0.00006575664,0.0001134128,0.00005621337,0.000133985,0.00001877831,0.000068464,0.00002314851,0.00002040438],"category_scores_gemma":[0.0001058261,0.00004486404,0.00006178522,0.0002215083,0.0003734322,0.00004653159,0.00003920195,0.00005948841,0.000006177054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001893769,"about_ca_system_score_gemma":0.00006070737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001112309,"about_ca_topic_score_gemma":0.00003216389,"domain_scores_codex":[0.9992194,0.000009225485,0.000145195,0.0001389964,0.0002919795,0.0001952342],"domain_scores_gemma":[0.999462,0.00008365222,0.00003373911,0.0002128158,0.00009612759,0.0001116676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003788066,0.0007382729,0.7205563,0.0003258234,0.0004734913,0.00008088164,0.001187096,0.0003443591,0.03668314,0.01837208,0.0004122996,0.2204475],"study_design_scores_gemma":[0.003822714,0.0005868242,0.9094619,0.0002738738,0.000622614,0.0003297204,0.004618466,0.04145323,0.03279291,0.0003160193,0.005361465,0.000360222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945359,0.0003661424,0.000279505,0.0003788164,0.00007939177,0.0001463724,0.000003034427,0.00002601637,0.004184876],"genre_scores_gemma":[0.9993804,0.0000670596,0.0001232567,0.0001269973,0.00003616617,4.455399e-7,0.000005548095,0.00000711442,0.000252993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2200872,"threshold_uncertainty_score":0.1829502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006043011487887632,"score_gpt":0.268824145055579,"score_spread":0.2627811335676914,"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."}}