{"id":"W4248024400","doi":"10.1121/1.4806741","title":"Efficient Bayesian multi-source localization using a graphics processing unit","year":2013,"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; Curse of dimensionality; Graphics processing unit; Gibbs sampling; Algorithm; Bayesian probability; Massively parallel; Computation; Source code; Sampling (signal processing); Artificial intelligence; Parallel computing","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":[0.000715272,0.0008300213,0.0007458861,0.0008767002,0.0004845305,0.001017254,0.001591724,0.0009603876,0.006125199],"category_scores_gemma":[0.002616307,0.0005828478,0.0006074758,0.000942369,0.0004489517,0.001159305,0.001420746,0.001069064,0.002815328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007896812,"about_ca_system_score_gemma":0.001325734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005321965,"about_ca_topic_score_gemma":0.00805387,"domain_scores_codex":[0.9994583,0.000130253,0.00002261775,0.00008118767,0.0002668286,0.0000407478],"domain_scores_gemma":[0.9993617,0.0002780824,0.00004589894,0.0001054542,0.000173144,0.00003577051],"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":[0.0003887492,0.0001026419,0.001487915,0.0001744995,0.0001158882,0.0002514088,0.0002874487,0.3870161,0.04666356,0.03203166,0.01002988,0.5214502],"study_design_scores_gemma":[0.00003096795,0.00002031429,0.0001658369,0.000008803094,0.000007599721,0.00004850126,0.0000193718,0.9826301,0.004809999,0.008165846,0.004076772,0.00001586781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001655099,0.00003639713,0.9961091,0.00005744959,0.000009785425,0.00001190095,0.00002630445,0.001406382,0.0006875528],"genre_scores_gemma":[0.06564961,0.00007499932,0.9322814,0.00006447625,0.00001516966,0.00009849847,0.000188116,0.0002779691,0.001349767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006125199,"threshold_uncertainty_score":0.02049088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02974137095274043,"score_gpt":0.2705122100974585,"score_spread":0.240770839144718,"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."}}