{"id":"W1916093793","doi":"","title":"Array Element Localization of a Bottom-Mounted Hydrophone Array Using Ship Noise","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research & Development Corporation","funders":"","keywords":"Hydrophone; Acoustics; Broadband; Noise (video); Underwater acoustics; Engineering; Computer science; Geology; Telecommunications; Underwater; Physics; Artificial intelligence; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003248336,0.0004947801,0.0004719849,0.0004488587,0.0002553546,0.0004799974,0.0003898316,0.0004952925,0.000852537],"category_scores_gemma":[0.001099607,0.0002925093,0.0001804348,0.000440482,0.0002290957,0.0005709853,0.0008798682,0.0004263913,0.000904811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092493,"about_ca_system_score_gemma":0.00041987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001655334,"about_ca_topic_score_gemma":0.002398709,"domain_scores_codex":[0.9996755,0.00007911356,0.00001029356,0.0000755447,0.0001199088,0.00003966919],"domain_scores_gemma":[0.9997445,0.000080133,0.00002166001,0.00002577403,0.0001075079,0.00002041715],"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":[0.001005672,0.00006162852,0.00589429,0.0001296033,0.00006099063,0.0002293648,0.0005889059,0.08568802,0.6832243,0.003128779,0.001157971,0.2188303],"study_design_scores_gemma":[0.00009022,0.0005263679,0.009472751,0.0000590257,0.00008350872,0.000443565,0.0001829966,0.7070195,0.2731517,0.002045098,0.006832506,0.00009280017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2021963,0.0001822106,0.7916042,0.0001696018,0.0001119545,0.00003088487,0.0002105435,0.0009696138,0.004524702],"genre_scores_gemma":[0.6030758,0.0001792622,0.3908313,0.0000626012,0.00003750142,0.00005736616,0.0003362685,0.00006053044,0.005359336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001655334,"threshold_uncertainty_score":0.003291428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03367929760909207,"score_gpt":0.2490848587545569,"score_spread":0.2154055611454649,"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."}}