{"id":"W1570185147","doi":"","title":"Bayesian localization of multiple ocean acoustic sources with environmental uncertainties","year":2011,"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":"University of Victoria","funders":"","keywords":"Curse of dimensionality; Bayesian probability; Inversion (geology); Modal; Environmental noise; Noise (video); Variance (accounting); Random variable; Computer science; Environmental science; Acoustics; Mathematics; Statistics; Geology; Physics; Artificial intelligence; Seismology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001260124,0.0001697672,0.0001693442,0.0002680862,0.0001922346,0.00003670366,0.0002884979,0.00009713579,0.002428627],"category_scores_gemma":[0.00004640247,0.0001436084,0.00002885972,0.0002019981,0.0004154093,0.0001194369,0.00001053915,0.0001469782,0.00005955725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004728162,"about_ca_system_score_gemma":0.0003580615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1207188,"about_ca_topic_score_gemma":0.1818361,"domain_scores_codex":[0.9986342,0.00004396042,0.0002120423,0.0002381556,0.0003411778,0.0005304984],"domain_scores_gemma":[0.9990351,0.00008596312,0.0000689965,0.000240286,0.00004707318,0.0005226138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003511006,0.00001639686,0.5928872,0.00004252733,0.00003261621,0.00006433194,0.001104487,0.403766,0.0003391268,0.000002068999,0.0003395345,0.001370675],"study_design_scores_gemma":[0.0002842733,0.000276576,0.1425389,0.00002507873,0.00005990701,0.000022378,0.002161557,0.853507,0.0004628911,0.0001952936,0.0002019535,0.0002641865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4786691,0.0001624586,0.5141811,0.00002552,0.000183516,0.0004123987,0.001024336,0.00004886268,0.005292661],"genre_scores_gemma":[0.9962308,0.0000330363,0.003112098,0.0001069096,0.00004708078,5.279063e-7,0.0001914865,0.0000138318,0.000264263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5175616,"threshold_uncertainty_score":0.9984833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698039266318212,"score_gpt":0.1830281360022323,"score_spread":0.1660477433390502,"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."}}