{"id":"W2596008051","doi":"10.1117/12.2255590","title":"Phase-factor based beamforming to improve the visualization of hyper-echoic targets","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Beamforming; Visualization; Phase (matter); Factor (programming language); Telecommunications; Artificial intelligence; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005714837,0.0009180427,0.0002946058,0.0005781865,0.0001285328,0.0004527775,0.0004552456,0.0006027176,0.003050155],"category_scores_gemma":[0.00141301,0.0003327575,0.0004471197,0.000621865,0.0003433836,0.001038037,0.0004539213,0.0004471592,0.001287052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002255404,"about_ca_system_score_gemma":0.0003511084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004240258,"about_ca_topic_score_gemma":0.0008824979,"domain_scores_codex":[0.999739,0.00007773494,0.00001575682,0.0000462113,0.0001051559,0.00001620675],"domain_scores_gemma":[0.9995171,0.0002253679,0.00006461975,0.00004500211,0.000125755,0.00002230862],"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":[0.0001602166,0.00006616107,0.0004781785,0.0001863355,0.00005657243,0.00008726768,0.00008810894,0.02973655,0.7335233,0.005641027,0.001323541,0.2286527],"study_design_scores_gemma":[0.00007613978,0.0004804776,0.002267008,0.00003994527,0.00009877775,0.001280738,0.00004450483,0.4549084,0.5197718,0.005763461,0.01514846,0.0001202444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00928891,0.000309654,0.9891872,0.00007807773,0.00003181534,0.00002007399,0.00003500461,0.0004684669,0.0005808526],"genre_scores_gemma":[0.06969123,0.0007172413,0.9274207,0.0001067385,0.00004321972,0.00004706415,0.0001439221,0.0001872148,0.001642674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003050155,"threshold_uncertainty_score":0.01020378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249245234105266,"score_gpt":0.2711458573893774,"score_spread":0.2586534050483247,"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."}}