{"id":"W2918709584","doi":"10.1109/ultsym.2018.8580187","title":"High Frame Rate Vector Flow Imaging: Development as a New Diagnostic Mode on a Clinical Scanner","year":2018,"lang":"en","type":"article","venue":"","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"","keywords":"Hfr cell; Scanner; Imaging phantom; Vector flow; Volumetric flow rate; Turbulence; Frame rate; Flow (mathematics); Flow velocity; Volume (thermodynamics); Physics; Computer science; Optics; Computer vision; Mechanics; Image (mathematics); Chemistry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002898526,0.0002305533,0.0003442437,0.0001519085,0.000116225,0.00005265102,0.0001143264,0.00007760817,0.00116327],"category_scores_gemma":[0.002232546,0.0001702882,0.0001409938,0.0002499082,0.0001972207,0.00006844063,0.00003540744,0.0003170655,0.002854475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004825543,"about_ca_system_score_gemma":0.0005404747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004900173,"about_ca_topic_score_gemma":0.00003626168,"domain_scores_codex":[0.9983667,0.00005832911,0.0004187207,0.000461361,0.0002717017,0.0004232595],"domain_scores_gemma":[0.998109,0.0006755528,0.00005980112,0.0003860453,0.0001329173,0.0006367032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0014107,0.001770127,0.257971,0.00005566666,0.0006610872,0.00009116044,0.002170663,0.00003915072,0.00345249,0.001239917,0.6175858,0.1135522],"study_design_scores_gemma":[0.005941321,0.002153133,0.2626424,0.0006168778,0.0002684679,0.0001295179,0.000137935,0.001487236,0.005161811,0.001765414,0.7189302,0.0007656129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9512703,0.0001032407,0.019215,0.01527135,0.001728334,0.0004053592,0.000003901633,0.0004266516,0.01157587],"genre_scores_gemma":[0.9102282,0.00003086463,0.06282976,0.01802514,0.001404439,0.00001829489,0.00002773919,0.00004585831,0.007389725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1127865,"threshold_uncertainty_score":0.9997498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961534354343291,"score_gpt":0.3219403466924563,"score_spread":0.3023250031490234,"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."}}