{"id":"W1969511862","doi":"10.1121/1.4920177","title":"Bayesian linearized two-hydrophone localization of a pulsed acoustic source","year":2015,"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":"Hydrophone; Acoustics; Inversion (geology); Nonlinear system; Underwater acoustics; Monte Carlo method; Bayesian probability; Geology; Computer science; Physics; Algorithm; Underwater; Mathematics; Statistics; Seismology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005332179,0.0005395529,0.0004137553,0.000403968,0.0002497659,0.0006894266,0.0008124398,0.0006228025,0.001487943],"category_scores_gemma":[0.002255033,0.0004929138,0.0004005036,0.0003629061,0.0007767655,0.0009397849,0.0009797273,0.0006897305,0.0005682015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007762697,"about_ca_system_score_gemma":0.00138441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006931496,"about_ca_topic_score_gemma":0.005858172,"domain_scores_codex":[0.9997973,0.00006987388,0.000008020149,0.00003894983,0.00006333394,0.00002268977],"domain_scores_gemma":[0.9993111,0.0004061924,0.0001047501,0.00003744229,0.0001082385,0.00003233113],"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":[0.00009729793,0.00001576103,0.0005998483,0.00006793546,0.00002388273,0.00006954696,0.00009806375,0.939638,0.008350079,0.01671476,0.000483635,0.03384123],"study_design_scores_gemma":[0.000005627681,0.000009980084,0.0001343341,0.000003739781,0.00000321076,0.00001732957,0.000005067041,0.9966196,0.0009790353,0.00203847,0.0001718101,0.00001182165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008349877,0.00003975465,0.9907658,0.00004982704,0.000004609384,0.000008598735,0.00003184354,0.0001828684,0.0005667375],"genre_scores_gemma":[0.6615903,0.0002944145,0.3313557,0.00008518764,0.00002984032,0.0001459533,0.0003490349,0.0001442666,0.006005372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006931496,"threshold_uncertainty_score":0.01378226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058185536652132,"score_gpt":0.2585333469221833,"score_spread":0.237951491555662,"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."}}