{"id":"W2119513145","doi":"10.1109/ccece.1995.528190","title":"Minimum distance processor for biological tissues classification from A-scan ultrasonic signals","year":2002,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Orthogonalization; Computer science; Ultrasonic sensor; Noise (video); Mathematics; Computer vision; Algorithm; Acoustics; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003464776,0.0001200757,0.0001403066,0.00003243068,0.00004119426,0.00002678514,0.0001268291,0.00008937854,0.0003555699],"category_scores_gemma":[0.00002554216,0.00008565247,0.00006590548,0.0002441813,0.00003206214,0.00006468493,0.000003323568,0.00006685398,0.00004911014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001857261,"about_ca_system_score_gemma":0.000001787511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004338764,"about_ca_topic_score_gemma":0.00001173574,"domain_scores_codex":[0.999314,0.000007852088,0.0001672822,0.000195731,0.00007541819,0.0002397171],"domain_scores_gemma":[0.9996514,0.0001361836,0.00002150762,0.0001021708,0.00002905305,0.00005970199],"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":[0.00006350423,0.000385996,0.01260871,0.0001605101,0.000165062,0.000002804194,0.0003442264,0.000179383,0.6920335,0.003624654,0.05818431,0.2322473],"study_design_scores_gemma":[0.001500561,0.0008028935,0.02983886,0.00009401,0.00007399113,0.000003294272,0.0002010088,0.4405754,0.3284007,0.01177628,0.1851698,0.001563187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8658579,0.0120113,0.1058743,0.0009957767,0.000214505,0.0008562413,0.0001656003,0.001601184,0.01242319],"genre_scores_gemma":[0.996538,0.0005648234,0.002031575,0.00005896036,0.0001328354,0.0001077126,0.00003215621,0.00001259595,0.000521345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.440396,"threshold_uncertainty_score":0.3893241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946906080006246,"score_gpt":0.2361101532406579,"score_spread":0.1966410924405954,"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."}}