{"id":"W1993172603","doi":"10.1121/1.3249244","title":"Bayesian tracking predictions in an uncertain ocean environment.","year":2009,"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":"Probabilistic logic; Range (aeronautics); Computer science; Bayesian probability; Markov chain Monte Carlo; Source tracking; Posterior probability; Sampling (signal processing); Tracking (education); Monte Carlo method; Joint probability distribution; Bayesian inference; Markov chain; Statistics; Mathematics; Artificial intelligence; Machine learning; Computer vision; Engineering","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":[0.002421911,0.0006501829,0.0007645965,0.0009743789,0.0004757539,0.001429848,0.001127751,0.001128898,0.001659167],"category_scores_gemma":[0.01495045,0.0007432895,0.000686278,0.0008465928,0.001021394,0.003192539,0.0009258421,0.0009649383,0.0004018843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104885,"about_ca_system_score_gemma":0.0009999607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0121678,"about_ca_topic_score_gemma":0.009508917,"domain_scores_codex":[0.9992872,0.0001959059,0.00003464045,0.0001511902,0.0002625484,0.00006855075],"domain_scores_gemma":[0.9951228,0.003614897,0.0005773095,0.0001870573,0.0003863937,0.0001114905],"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":[0.00002999276,0.000007995289,0.001870836,0.00002733149,0.00002324195,0.00005494666,0.00004527256,0.9677556,0.0003299834,0.01877698,0.0003107497,0.01076723],"study_design_scores_gemma":[0.000004934521,0.000009341278,0.0006061745,0.00001223102,0.000006487674,0.00002514141,0.00001329685,0.9761401,0.0001908428,0.02269446,0.0002845166,0.00001242501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05766023,0.000470033,0.9373197,0.0004722017,0.00005803598,0.00002262329,0.0002324561,0.0002065064,0.003558291],"genre_scores_gemma":[0.900278,0.0007750374,0.09536501,0.0001219231,0.00008401673,0.00005977821,0.0006343359,0.00009816736,0.002583754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0121678,"threshold_uncertainty_score":0.024194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052989556973351,"score_gpt":0.2587430475807782,"score_spread":0.2382131520110446,"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."}}