{"id":"W2013858643","doi":"10.1121/1.4805438","title":"Classifying sonar signals with varying signal-to-noise ratio and bandwidth","year":2013,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Sonar; Computer science; Bandwidth (computing); Broadband; Acoustics; Classifier (UML); Marine mammals and sonar; Clutter; Speech recognition; Transducer; Artificial intelligence; Pattern recognition (psychology); Radar; Telecommunications; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001146945,0.0004706499,0.0005759205,0.0009723021,0.0002052584,0.0007622445,0.0003120985,0.0007960474,0.0004317916],"category_scores_gemma":[0.009027989,0.0001800534,0.0002582678,0.0005463497,0.0004816182,0.0008859779,0.0004048993,0.0004031011,0.0002519137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002214333,"about_ca_system_score_gemma":0.0001375101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006802263,"about_ca_topic_score_gemma":0.0007710382,"domain_scores_codex":[0.9993298,0.0001361989,0.00008614727,0.0001225848,0.0002016881,0.0001236698],"domain_scores_gemma":[0.9951014,0.003427945,0.0003364503,0.0002065415,0.0008205679,0.0001071919],"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":[0.003105735,0.0005869709,0.02210214,0.0002775339,0.0001393196,0.0003477505,0.0003776794,0.04891157,0.7258981,0.0004903296,0.0002775122,0.1974853],"study_design_scores_gemma":[0.0001308056,0.003360344,0.1093704,0.00005782403,0.0002316321,0.000817931,0.0006377789,0.382234,0.5013081,0.0008671741,0.0008376272,0.0001463233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980018,0.0002169126,0.01887051,0.0000407515,0.00002199654,0.00002706931,0.0000326755,0.000177673,0.0005944199],"genre_scores_gemma":[0.9808885,0.000160685,0.01835311,0.00004329866,0.00001449752,0.00002899982,0.00009450399,0.00003189295,0.0003845248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001146945,"threshold_uncertainty_score":0.006065667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040105317112764,"score_gpt":0.2402347724810596,"score_spread":0.219833719309932,"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."}}