{"id":"W3042328353","doi":"10.35940/ijeat.c6054.029320","title":"Underwater Object Localization using the Spinning Propeller Noise of Ships Based on the Wittekind Model","year":2020,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Underwater; Acoustics; Propeller; Hydrophone; Noise (video); Reflection (computer programming); Sonar; Anechoic chamber; Computer science; Narrowband; Multipath propagation; Channel (broadcasting); Marine engineering; Engineering; Geology; Physics; Computer vision; 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":[0.0001061214,0.00008553896,0.0001146943,0.0001217971,0.0000302164,0.00002071512,0.0003321721,0.00004717376,0.000002327166],"category_scores_gemma":[0.00002179235,0.00005199134,0.00003549573,0.0001376526,0.00003764603,0.00006938523,0.00003784061,0.0002310002,3.977514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002930018,"about_ca_system_score_gemma":0.00001311316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.639712e-7,"about_ca_topic_score_gemma":1.864392e-7,"domain_scores_codex":[0.9994329,0.00001125429,0.000267252,0.00005618896,0.0001545204,0.00007790218],"domain_scores_gemma":[0.9996096,0.00004516937,0.0000972784,0.000102944,0.0001239949,0.00002104809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001237712,0.000005403749,0.0000794838,0.00001182846,0.0000374398,0.000001590174,0.000153838,0.9163348,0.08139578,0.0005855882,0.000003974475,0.001377868],"study_design_scores_gemma":[0.0002308193,0.00003132081,0.00001131232,0.0001276843,0.000006388796,0.00001834768,0.0001642481,0.9408422,0.05732726,0.000212069,0.0009736983,0.00005469521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2269615,0.0002182364,0.7703632,0.002261426,0.00006300821,0.00005973774,0.000001552563,0.00003974387,0.00003169901],"genre_scores_gemma":[0.9937417,0.00004687826,0.00603062,0.0001204287,0.0000401825,0.000001857427,5.419028e-7,0.00001601476,0.000001765241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7667803,"threshold_uncertainty_score":0.2120145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02398485546943734,"score_gpt":0.2303959588332692,"score_spread":0.2064111033638319,"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."}}