{"id":"W2945661281","doi":"10.1016/j.ultrasmedbio.2019.04.001","title":"Acoustic Shadow Detection: Study and Statistics of B-Mode and Radiofrequency Data","year":2019,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nakagami distribution; Computer science; Thresholding; Transducer; Artificial intelligence; Standard deviation; Brightness; Speckle pattern; Entropy (arrow of time); Artifact (error); Computer vision; Acoustics; Statistics; Algorithm; Physics; Optics; Mathematics; Image (mathematics); Fading","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":[],"consensus_categories":[],"category_scores_codex":[0.0003385508,0.0001016303,0.0003006008,0.0001218359,0.00001431234,0.000003350876,0.0001165566,0.00004505097,0.00008911281],"category_scores_gemma":[0.0002860386,0.00008069853,0.000005355107,0.0001403862,0.0001049517,0.00004872978,0.00002166644,0.0001269606,0.00000214332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001468253,"about_ca_system_score_gemma":0.000006884813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003117766,"about_ca_topic_score_gemma":0.0008528637,"domain_scores_codex":[0.9993151,0.00004371836,0.0002442687,0.0001943639,0.00007556343,0.0001269725],"domain_scores_gemma":[0.9993487,0.0002959749,0.00002987551,0.0002679524,0.00002317948,0.00003434482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001809461,0.00005809164,0.5696296,0.0001886874,0.0001768344,0.000004857826,0.000997807,0.0004417284,0.3718569,0.00008972453,0.0002769467,0.05626073],"study_design_scores_gemma":[0.01414479,0.006721016,0.6251183,0.00048021,0.00168454,0.000149999,0.009404491,0.3253437,0.001685086,0.007204131,0.006416823,0.001646839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860259,0.002495317,0.01063637,0.00001819,0.000232987,0.0001766652,0.00004410461,0.00002430958,0.0003461147],"genre_scores_gemma":[0.9983742,0.0009993655,0.0004576652,0.00001577454,0.00006463316,0.000002860977,0.00006147844,0.000008499184,0.00001551739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3701718,"threshold_uncertainty_score":0.329079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134560822390113,"score_gpt":0.2802069708630165,"score_spread":0.2588613626391154,"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."}}