{"id":"W2743144186","doi":"10.1088/1361-6560/aa7fe7","title":"Intraventricular vector flow mapping—a Doppler-based regularized problem with automatic model selection","year":2017,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôtel-Dieu de Montréal; Canada Research Chairs; University of Toronto; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vector flow; Vector field; Mathematics; Algorithm; Mathematical analysis; Discretization; Doppler effect; Curl (programming language); Regularization (linguistics); Computer science; Applied mathematics; Artificial intelligence; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002968967,0.0001669916,0.0005179258,0.0001091423,0.0001546976,0.00001194936,0.00006588626,0.0001158892,0.00002707344],"category_scores_gemma":[0.00008138652,0.000100271,0.00007476378,0.0001377704,0.0002804611,0.0000442243,0.00002253098,0.0002438743,0.000002716659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004959258,"about_ca_system_score_gemma":0.0000966958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001413345,"about_ca_topic_score_gemma":0.00002687129,"domain_scores_codex":[0.9990968,0.00005562519,0.0002043536,0.0002947521,0.0001333647,0.0002151248],"domain_scores_gemma":[0.9993364,0.0000365743,0.0001212087,0.0003263083,0.00008689221,0.00009261903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001785714,0.001257383,0.5757527,0.001831898,0.002195422,0.000213946,0.002203401,0.009050142,0.09220382,0.01128137,0.004614331,0.2976099],"study_design_scores_gemma":[0.01335695,0.001261722,0.07437083,0.0006146595,0.0004464033,0.000187993,0.0001352402,0.9015529,0.001276385,0.004586962,0.001874323,0.0003356443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7866521,0.0002448626,0.2042824,0.006123087,0.0002754484,0.001057125,0.000005279803,0.0001382018,0.00122139],"genre_scores_gemma":[0.9906471,0.0001110448,0.008287261,0.0003930762,0.0003639085,0.00003717728,0.0000646382,0.00001613511,0.00007959548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8925027,"threshold_uncertainty_score":0.4088932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062533794014289,"score_gpt":0.3269416479029989,"score_spread":0.22068826850157,"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."}}