{"id":"W2080147960","doi":"10.1121/1.4794931","title":"Bayesian tracking of multiple acoustic sources in an uncertain ocean environment","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":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bayesian probability; Markov chain Monte Carlo; Computer science; Range (aeronautics); Monte Carlo method; Marginal distribution; Tracking (education); Noise (video); Posterior probability; Joint probability distribution; Variance (accounting); Markov chain; Statistics; Mathematics; Artificial intelligence; Machine learning; Random variable","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":[0.001347949,0.0004623315,0.0007409164,0.0009343295,0.0004532897,0.0009627719,0.001081165,0.0009405848,0.0006099874],"category_scores_gemma":[0.006622146,0.0008252028,0.0004781136,0.0007538289,0.0008822427,0.002150011,0.001535227,0.0009930539,0.000232281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009363274,"about_ca_system_score_gemma":0.001412131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00718045,"about_ca_topic_score_gemma":0.007484118,"domain_scores_codex":[0.9994195,0.0001298688,0.00003221681,0.0001401959,0.0002262875,0.00005212768],"domain_scores_gemma":[0.998629,0.0008626392,0.0001973041,0.00008244508,0.0001789194,0.00004980871],"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":[0.00004881658,0.00001902718,0.001111844,0.00003136552,0.00003015438,0.00006006649,0.00006878289,0.9323658,0.002713907,0.01892888,0.0002506265,0.04437068],"study_design_scores_gemma":[0.000004703649,0.000007701683,0.0002629621,0.000006885871,0.000004671734,0.00001737466,0.000006327164,0.989773,0.0006779721,0.008941733,0.0002870781,0.000009665842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01105277,0.00008238271,0.9882005,0.00006240376,0.00000951212,0.00000693901,0.00002147049,0.00007837762,0.0004856894],"genre_scores_gemma":[0.6639212,0.0005096364,0.3325481,0.0001013317,0.00006286868,0.00006784428,0.0002124754,0.00008464335,0.002492052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00718045,"threshold_uncertainty_score":0.01427734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053326830516676,"score_gpt":0.2423216978816959,"score_spread":0.2217884295765292,"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."}}