{"id":"W2341994406","doi":"10.1109/tuffc.2016.2551705","title":"High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs","keywords":"Waveform; Bandwidth (computing); Computer science; Autocorrelation; Frame rate; Transmission (telecommunications); Image resolution; Synthetic aperture radar; Optics; Electronic engineering; Acoustics; Artificial intelligence; Physics; Engineering; Telecommunications; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006654935,0.0005492114,0.0007001524,0.0005411239,0.0003808989,0.0001275798,0.0001526884,0.0001881521,0.00005852701],"category_scores_gemma":[0.000400609,0.0004099066,0.000280908,0.0008157981,0.0001429789,0.0003213634,8.123079e-7,0.0006514378,0.0000107055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004087309,"about_ca_system_score_gemma":0.0001864089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002661177,"about_ca_topic_score_gemma":0.00004154314,"domain_scores_codex":[0.9971735,0.0001536424,0.0006380331,0.0008213284,0.0004516269,0.0007618815],"domain_scores_gemma":[0.9967546,0.00178913,0.0001716058,0.0005998596,0.0003189603,0.0003657909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006670147,0.00173682,0.008531004,0.00008606136,0.000566714,0.00002396203,0.0006795207,0.0005808284,0.9797377,0.00014863,0.00001199543,0.00722977],"study_design_scores_gemma":[0.1921702,0.02702079,0.2008872,0.004927583,0.02168194,0.009115845,0.005163725,0.4156192,0.09392802,0.01313496,0.001668074,0.01468237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5076085,0.0002527631,0.4900222,0.0005477763,0.0003702137,0.0008920276,0.00009022051,0.0001908903,0.00002540627],"genre_scores_gemma":[0.9953865,0.0002024518,0.00363584,0.0004289302,0.00008529592,0.00009171978,0.00000803177,0.00009149896,0.00006976908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8858097,"threshold_uncertainty_score":0.9998353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176016302085096,"score_gpt":0.2376097811028798,"score_spread":0.2258496180820288,"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."}}