{"id":"W4318814655","doi":"10.36227/techrxiv.21967823.v1","title":"Underwater Source Localization via Spectral Element Acoustic Field Estimation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mitel (Canada)","funders":"","keywords":"Estimator; Underwater; Kalman filter; Discretization; Filter (signal processing); Computer science; Acoustics; Dimension (graph theory); Underwater acoustics; Algorithm; Null (SQL); Position (finance); Mathematics; Physics; Geology; Mathematical analysis; Statistics; Artificial intelligence; Computer vision","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":[0.000525221,0.0005701043,0.000572769,0.0005205413,0.0002083959,0.0004928523,0.0008552453,0.0006931125,0.001044938],"category_scores_gemma":[0.001840658,0.0003817245,0.0004542191,0.0005611409,0.000420076,0.001514182,0.00100798,0.0006312317,0.0005215582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003517357,"about_ca_system_score_gemma":0.0006770932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00212574,"about_ca_topic_score_gemma":0.002037535,"domain_scores_codex":[0.9996645,0.00007785141,0.00001544739,0.00007990903,0.0001352517,0.00002695779],"domain_scores_gemma":[0.9995258,0.000198387,0.00007817203,0.00006429133,0.0001188235,0.00001458932],"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":[0.00008703913,0.00005876509,0.001088914,0.0001008478,0.00004945256,0.00005716817,0.00008671203,0.7507367,0.02921375,0.01653032,0.000834386,0.201156],"study_design_scores_gemma":[0.000003218573,0.00001595916,0.0000947092,0.000003925018,0.000004367927,0.00001910902,0.000005845578,0.9934703,0.003925627,0.001988982,0.0004623316,0.000005575687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003147086,0.00002478994,0.9964903,0.00002108816,0.000007606239,0.000004465328,0.000008419211,0.0001307541,0.0001654283],"genre_scores_gemma":[0.3346907,0.0003033714,0.6622609,0.00006586555,0.00003841859,0.00009566359,0.0001635379,0.00006485101,0.002316719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00212574,"threshold_uncertainty_score":0.004226744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544093972252899,"score_gpt":0.2781748277296744,"score_spread":0.2427338880071454,"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."}}