{"id":"W2346466976","doi":"10.1121/1.4950534","title":"Bayesian localization, tracking, and environmental inference","year":2016,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Gibbs sampling; Prior probability; Computer science; Markov chain Monte Carlo; Bayesian probability; Bayesian inference; Parallel tempering; Sampling (signal processing); Source tracking; Inference; Posterior probability; Environmental noise; Algorithm; Artificial intelligence; Hybrid Monte Carlo; Computer vision; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004263099,0.00007548314,0.0001364425,0.00001204272,0.0001600143,0.0000180894,0.000400512,0.0000368599,0.0006013631],"category_scores_gemma":[0.0001450266,0.00002896512,0.0000893346,0.00009875714,0.001118958,0.0001077562,0.00005014722,0.0001680574,0.000006395234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001079023,"about_ca_system_score_gemma":0.00004868446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008370868,"about_ca_topic_score_gemma":0.000004154614,"domain_scores_codex":[0.998907,0.0001340866,0.0002434736,0.00006332176,0.0004700006,0.0001820965],"domain_scores_gemma":[0.9987813,0.0007561265,0.0001830241,0.0001425403,0.00004159075,0.0000954366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002649961,0.0001539149,0.2812427,0.00006908071,0.0003169496,0.000004131875,0.002991202,0.1794032,0.0324404,0.000002002185,0.01557384,0.4875376],"study_design_scores_gemma":[0.0004144734,0.0003718026,0.04424226,0.00007119543,0.0001112103,0.00007903617,0.001316249,0.9475784,0.00146083,0.002532787,0.001704421,0.0001173222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01245649,0.0001673047,0.9845811,0.002577726,0.00005426422,0.00005854222,0.00002695172,0.00000283023,0.00007481324],"genre_scores_gemma":[0.9926177,0.001073016,0.005708873,0.000461016,0.00006387923,5.061994e-8,4.855156e-7,0.00000287794,0.00007209062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9801612,"threshold_uncertainty_score":0.6584504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01349040895729825,"score_gpt":0.2386015851195445,"score_spread":0.2251111761622462,"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."}}