{"id":"W3083344166","doi":"10.1109/access.2020.3021685","title":"Phase Aberration Correction: A Convolutional Neural Network Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Channel (broadcasting); Algorithm; Phase (matter); Pattern recognition (psychology); Artificial neural network; Computer vision; Telecommunications; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004710108,0.00009254851,0.00009057116,0.00001528206,0.00007568932,0.00009926333,0.0001196612,0.00004719185,0.00005700014],"category_scores_gemma":[0.00001534208,0.00009578653,0.00003099249,0.0002186932,0.00001483843,0.000300272,0.00001049069,0.0001411464,0.00002064756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002687497,"about_ca_system_score_gemma":0.00001317543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000410208,"about_ca_topic_score_gemma":0.000001086394,"domain_scores_codex":[0.9994398,0.0000121075,0.0001441358,0.0001331611,0.0001204369,0.0001503597],"domain_scores_gemma":[0.9997736,0.00002325577,0.00002706495,0.00006507635,0.00003875332,0.00007223505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001437087,0.0000334848,0.0001175317,0.00002591828,0.00001715902,0.000001264599,0.0000819929,0.9479973,0.003454876,0.00008041098,0.04356457,0.004611162],"study_design_scores_gemma":[0.0004878616,0.00003710024,0.0002280492,0.000004653533,0.00001405202,0.000005811898,0.0000108794,0.9963514,0.001183955,0.00009223903,0.001469543,0.0001144625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1630591,0.0001791643,0.8289317,0.0002067054,0.002832045,0.0002049299,0.00001059962,0.0003837461,0.004191987],"genre_scores_gemma":[0.9976709,0.00001181266,0.0005466685,0.0002707881,0.001365105,0.00002384238,0.0000623548,0.00001714416,0.00003141482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8346118,"threshold_uncertainty_score":0.3906062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03459068900973159,"score_gpt":0.2622399204280376,"score_spread":0.227649231418306,"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."}}