{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004470285,0.00004200153,0.00006067843,0.00007146705,0.00002053671,0.00002133343,0.00001362846,0.00002991383,0.0002885764],"category_scores_gemma":[8.763668e-7,0.00004037701,0.00001340091,0.00006557452,0.000004816753,0.00009089367,0.0000057539,0.00007686187,0.00004370757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000250358,"about_ca_system_score_gemma":9.520916e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003945403,"about_ca_topic_score_gemma":0.0001167272,"domain_scores_codex":[0.9997603,0.00001271797,0.00005272793,0.00007032699,0.00003365861,0.00007031437],"domain_scores_gemma":[0.9999306,0.000005964461,0.000007577929,0.00003351939,0.000005754663,0.00001655111],"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":[0.00000140475,0.00002120169,0.1489167,0.00006049871,0.00002860833,0.000001175425,0.0002614527,0.02809487,0.7609355,0.0002019764,0.000005504543,0.06147107],"study_design_scores_gemma":[0.0001537247,0.000004558054,0.161524,0.00000884194,0.000006208376,0.000001037854,0.0002465387,0.8350193,0.0026833,0.00007897161,0.0001886521,0.00008487222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879446,0.0000106202,0.005452303,0.00001334696,0.000009973592,0.00002783713,8.091835e-8,0.00005897238,0.006482268],"genre_scores_gemma":[0.9992766,0.000007870996,0.0003149861,0.000007743071,0.000006954524,9.11069e-7,0.000002293158,0.000006910632,0.0003756754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8069244,"threshold_uncertainty_score":0.3159709,"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."}}