{"id":"W3006292530","doi":"10.1145/3366486.3366509","title":"Human Activity Classification in Underwater using Sonar and Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Spectrogram; Sonar; Convolutional neural network; Underwater; Computer science; Artificial intelligence; Doppler effect; Pattern recognition (psychology); SIGNAL (programming language); Feature extraction; Feature (linguistics); Waveform; Underwater acoustics; Acoustics; Speech recognition; Geology; Radar; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002539721,0.0004625568,0.0004509273,0.0006774085,0.0001352916,0.0003422207,0.0002803911,0.0003835121,0.0007975552],"category_scores_gemma":[0.0005639473,0.000168086,0.0002812039,0.0005226062,0.0002133989,0.0004221129,0.0003540772,0.0002709414,0.0003418522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884484,"about_ca_system_score_gemma":0.0001676053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002740276,"about_ca_topic_score_gemma":0.00375614,"domain_scores_codex":[0.9998363,0.00003149883,0.0000109054,0.00004740074,0.00003733614,0.00003660823],"domain_scores_gemma":[0.9998272,0.00007005031,0.00002824723,0.00001289473,0.00004596658,0.00001558411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004393983,0.0004157822,0.04312669,0.0001823671,0.0001540421,0.0003817923,0.000196612,0.1799271,0.06150063,0.000801693,0.001570205,0.7113037],"study_design_scores_gemma":[0.000004954034,0.0001429355,0.01746072,0.00001679004,0.00001809556,0.00009484145,0.0001103407,0.9736661,0.007362194,0.0005775564,0.0005352689,0.0000102661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6675709,0.000911454,0.3274818,0.0002223827,0.0001043131,0.0000482624,0.000256093,0.0007538811,0.002650801],"genre_scores_gemma":[0.955817,0.000286518,0.04098625,0.00008160609,0.0000396714,0.00002987708,0.0003816073,0.00002383531,0.002353613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002740276,"threshold_uncertainty_score":0.005448639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777330386598904,"score_gpt":0.2459199294388786,"score_spread":0.2181466255728896,"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."}}