{"id":"W4385698426","doi":"10.3389/fmars.2023.1211234","title":"DOA estimation of underwater acoustic co-frequency sources for the coprime vector sensor array","year":2023,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Polit National Laboratory for Marine Science and Technology","keywords":"Coprime integers; Hydrophone; Direction of arrival; Computer science; Acoustics; Underwater; SIGNAL (programming language); Algorithm; Grating; Physics; Optics; Telecommunications; Geology","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":[0.001064013,0.0001039941,0.0001465418,0.0002963234,0.0002882024,0.0001831368,0.001250179,0.00002950133,0.000005012387],"category_scores_gemma":[0.0003468875,0.00007306324,0.00003802419,0.001690916,0.0004549985,0.0005730911,0.000201515,0.00008810412,0.00001077777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006773965,"about_ca_system_score_gemma":0.000213881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985843,"about_ca_topic_score_gemma":0.000002777637,"domain_scores_codex":[0.9985946,0.0000205722,0.0002205377,0.0003569353,0.0004117905,0.0003955068],"domain_scores_gemma":[0.9992079,0.0001541417,0.0001135719,0.0003795234,0.00009209604,0.00005283944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003818301,0.00009165502,0.0961752,0.000267937,0.00003039661,0.00001417452,0.00394002,0.08528969,0.4890606,0.001135853,0.006353888,0.3176024],"study_design_scores_gemma":[0.0004799425,0.0000988898,0.04063077,0.00005490636,0.00001130498,0.000007936099,0.0004443397,0.3730178,0.550226,0.03455091,0.0002105106,0.0002666998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09223676,0.00002472079,0.9052635,0.0009333442,0.0008094815,0.0002289016,0.000002300398,0.00009100302,0.0004099236],"genre_scores_gemma":[0.5169194,0.000008432553,0.4826714,0.00007556815,0.00002935266,0.00001780909,0.000001555559,0.000004862923,0.0002716188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4246827,"threshold_uncertainty_score":0.2979432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140656979676054,"score_gpt":0.2630131960549137,"score_spread":0.2489474980873083,"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."}}