{"id":"W1965103226","doi":"10.1121/1.2933972","title":"Optimal source tracking in an unknown ocean environment","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; Viterbi algorithm; Algorithm; Markov chain Monte Carlo; Mathematical optimization; Range (aeronautics); Bayesian optimization; Track (disk drive); Bayesian probability; Monte Carlo method; Hidden Markov model; Artificial intelligence; Mathematics; Statistics","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":[0.0008996602,0.0006038605,0.0009141358,0.0007266589,0.0004100726,0.001078146,0.0009371217,0.001000544,0.0008795132],"category_scores_gemma":[0.004475417,0.000745556,0.0005067951,0.0006272442,0.0009670776,0.001883821,0.001311223,0.0007085368,0.0004371471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008013926,"about_ca_system_score_gemma":0.001421599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005979854,"about_ca_topic_score_gemma":0.005018776,"domain_scores_codex":[0.9995154,0.00008783319,0.00002247369,0.0001679109,0.0001453672,0.0000609197],"domain_scores_gemma":[0.9989705,0.0006542217,0.0001165856,0.00008862236,0.000137332,0.00003273765],"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":[0.00009599973,0.000024766,0.0008697786,0.00004639841,0.00002984169,0.00004105919,0.000110364,0.910318,0.004796242,0.01174287,0.0003545845,0.07157014],"study_design_scores_gemma":[0.00001419426,0.0000185948,0.0002622726,0.000007738083,0.000008111004,0.00002248354,0.00001415368,0.9891447,0.002163512,0.007845483,0.0004839912,0.00001478699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01478129,0.000118078,0.9840053,0.00005571024,0.00001146846,0.000007931296,0.0000211345,0.0002034491,0.0007955952],"genre_scores_gemma":[0.4412329,0.0003612227,0.5555836,0.00007146443,0.00003602228,0.00006320377,0.0001929291,0.0001658157,0.002292779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005979854,"threshold_uncertainty_score":0.01189011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02539072517522863,"score_gpt":0.2498333905854154,"score_spread":0.2244426654101868,"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."}}