{"id":"W1574297878","doi":"","title":"Bayesian tracking 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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bayesian probability; Source tracking; Curse of dimensionality; Seabed; Likelihood function; Computer science; Inversion (geology); Variance (accounting); Noise (video); Underwater acoustics; Acoustics; Algorithm; Geology; Estimation theory; Artificial intelligence; Underwater; Physics; 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":[0.002049987,0.0007428844,0.001041958,0.000916801,0.0005748292,0.001373049,0.00122892,0.001344702,0.0007813091],"category_scores_gemma":[0.008301222,0.00116755,0.00064596,0.001184248,0.001004519,0.002194646,0.002075059,0.001403438,0.0002615165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009396721,"about_ca_system_score_gemma":0.001587164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007914967,"about_ca_topic_score_gemma":0.009089881,"domain_scores_codex":[0.9990728,0.0002190461,0.00004446713,0.0002198158,0.000369422,0.00007442995],"domain_scores_gemma":[0.9974612,0.001616551,0.0003653961,0.0001472702,0.0003442305,0.00006536781],"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":[0.00005968436,0.00002434223,0.001407141,0.00004585132,0.0000480966,0.00009748172,0.0001126021,0.9236923,0.002488077,0.02311725,0.0003857364,0.04852141],"study_design_scores_gemma":[0.00000677555,0.000008821255,0.0003538467,0.000008924922,0.000008090748,0.00002169274,0.00001182939,0.9862908,0.0004979819,0.01229977,0.0004779445,0.00001349629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01218278,0.0001362153,0.9864324,0.0001086498,0.00001506549,0.00001155402,0.00003459606,0.00009297708,0.0009858778],"genre_scores_gemma":[0.6248438,0.0005791182,0.3691913,0.0001208082,0.00008856368,0.0001177278,0.0003528847,0.0001206107,0.00458514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007914967,"threshold_uncertainty_score":0.01573777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238802979432978,"score_gpt":0.1915533177651924,"score_spread":0.1676730198218946,"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."}}