{"id":"W1943028203","doi":"10.1515/itit-2015-0011","title":"Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets","year":2015,"lang":"en","type":"article","venue":"it - Information Technology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Stroke (engine); Segmentation; Computer science; Cerebral blood flow; Blood flow; High resolution; Medicine; Artificial intelligence; Radiology; Cardiology","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":[0.0002775753,0.00009693842,0.0002349938,0.001226717,0.00005220262,0.00007771815,0.0005635095,0.0001080683,0.00001243634],"category_scores_gemma":[0.0003221589,0.00009250626,0.00004302735,0.001423372,0.0002203076,0.001436492,0.0002534133,0.0000976652,0.000008374743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003632305,"about_ca_system_score_gemma":0.0001155935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001966149,"about_ca_topic_score_gemma":0.000003411832,"domain_scores_codex":[0.9989366,0.00002732462,0.0004239214,0.0001395957,0.0003299113,0.0001426422],"domain_scores_gemma":[0.9988285,0.00003979514,0.0002386925,0.0006168089,0.0001734703,0.0001026925],"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":[0.00003235907,0.0003517135,0.01295682,0.000294041,0.001065497,0.00000829399,0.003424106,0.04228114,0.0005109009,0.111618,0.02887237,0.7985848],"study_design_scores_gemma":[0.0004566699,0.00006509315,0.0001626563,0.00001334043,0.0001136399,0.000001945501,0.0001532122,0.9888574,0.007209922,0.002175007,0.0006836234,0.0001074199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004000928,0.00004185346,0.9944061,0.0007792192,0.00002786729,0.0001345721,0.00007359205,0.0004138014,0.0001220909],"genre_scores_gemma":[0.5556924,0.00001342401,0.44257,0.001203157,0.000002383649,0.00003710602,0.0004752452,0.000003367652,0.00000290242],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9465764,"threshold_uncertainty_score":0.3772296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972760063449033,"score_gpt":0.283388406725437,"score_spread":0.2636608060909467,"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."}}