{"id":"W2913827985","doi":"10.1002/mp.13437","title":"High frame rate doppler ultrasound bandwidth imaging for flow instability mapping","year":2019,"lang":"en","type":"article","venue":"Medical Physics","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"Canadian Institutes of Health Research; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Doppler effect; Instability; Frame rate; Bandwidth (computing); Medical imaging; Ultrasound; Doppler ultrasound; Radiology; Optics; Physics; Medical physics; Medicine; Computer science; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001301446,0.00063068,0.0004687542,0.001576272,0.0002299542,0.0007809894,0.000512614,0.0009484754,0.001013153],"category_scores_gemma":[0.002962692,0.0003049137,0.0003110259,0.0006821582,0.0005704212,0.0007835993,0.0005273872,0.0008266053,0.0003443419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004487891,"about_ca_system_score_gemma":0.0004725393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007968898,"about_ca_topic_score_gemma":0.0009780579,"domain_scores_codex":[0.9995381,0.0001699373,0.00002003805,0.00008006444,0.000153726,0.00003824973],"domain_scores_gemma":[0.9991334,0.0003880379,0.0001882203,0.00006784473,0.0001698917,0.00005265449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003760952,0.0001651226,0.01073087,0.0005533435,0.000062559,0.0002347808,0.0001676518,0.007006975,0.717188,0.002976379,0.001478565,0.2590597],"study_design_scores_gemma":[0.00007581637,0.001138172,0.05094381,0.0003238657,0.0002741322,0.003842927,0.0001801302,0.3140458,0.6037828,0.005755291,0.01941006,0.0002271641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1391079,0.009999559,0.8449817,0.000551787,0.0001379082,0.0002045847,0.0002675866,0.001343131,0.003405839],"genre_scores_gemma":[0.5714716,0.005363678,0.4209294,0.0003598447,0.0001772942,0.0002891364,0.0002371034,0.0001115347,0.00106052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001576272,"threshold_uncertainty_score":0.006882846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009441999996589281,"score_gpt":0.2456507321490718,"score_spread":0.2362087321524825,"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."}}