{"id":"W2022780326","doi":"10.1121/1.4808986","title":"Bayesian localization and tracking with environmental and array-element uncertainties.","year":2008,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","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":"Computer science; Algorithm; Bayesian probability; Markov chain Monte Carlo; Mathematical optimization; Sensor array; Monte Carlo method; Sampling (signal processing); Realization (probability); Mathematics; Statistics; Artificial intelligence; Machine learning","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":[0.004193079,0.001207557,0.00139174,0.001511634,0.0007811892,0.001831829,0.001729987,0.002289737,0.001931538],"category_scores_gemma":[0.01802172,0.001396089,0.001058145,0.002124809,0.001711152,0.00347586,0.002564075,0.001530531,0.0009121556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001670217,"about_ca_system_score_gemma":0.002342416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023688,"about_ca_topic_score_gemma":0.01074857,"domain_scores_codex":[0.9980528,0.0007364399,0.00008117408,0.0003601726,0.0006514175,0.0001179931],"domain_scores_gemma":[0.9956488,0.003033169,0.0004963822,0.0003475964,0.0003907233,0.00008337104],"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":[0.00006124103,0.00003482089,0.001089306,0.0001144089,0.0001027113,0.00009100068,0.000110959,0.8167431,0.001125223,0.1005759,0.001219662,0.07873172],"study_design_scores_gemma":[0.00001024072,0.00001459871,0.0003173705,0.00002280335,0.00001793943,0.00005824022,0.00001428846,0.946506,0.000578605,0.0500113,0.00242997,0.00001869167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001195127,0.0002463405,0.9974543,0.00007953361,0.00001646786,0.0000125145,0.00002563228,0.00007652809,0.0008935427],"genre_scores_gemma":[0.2484637,0.001762369,0.7405091,0.0002409568,0.0002368761,0.0002588362,0.000587651,0.0001702403,0.007770177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023688,"threshold_uncertainty_score":0.02217543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013465771778074,"score_gpt":0.2162709815065073,"score_spread":0.2028052097284332,"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."}}