{"id":"W2027028088","doi":"10.1121/1.4757639","title":"Parallel tempering for strongly nonlinear geoacoustic inversion","year":2012,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Parallel tempering; Markov chain Monte Carlo; Metropolis–Hastings algorithm; Sampling (signal processing); Nonlinear system; Bayesian probability; Algorithm; Markov chain; Computer science; Gibbs sampling; Importance sampling; Initialization; Inversion (geology); Monte Carlo method; Mathematics; Statistics; Hybrid Monte Carlo; Artificial intelligence; Physics; Geology; Machine learning; Telecommunications","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":[0.001961558,0.0008191844,0.0008708611,0.0003552616,0.0006233241,0.0007108085,0.001136393,0.000677538,0.002696919],"category_scores_gemma":[0.006586904,0.0005798007,0.0008471936,0.0005090194,0.001111678,0.001054404,0.001281844,0.001621895,0.0007656059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938498,"about_ca_system_score_gemma":0.0009951466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004232998,"about_ca_topic_score_gemma":0.004361069,"domain_scores_codex":[0.9993014,0.0003046126,0.00004229924,0.00009946367,0.0002051341,0.00004701446],"domain_scores_gemma":[0.998502,0.0008267161,0.0001116289,0.000275645,0.000218646,0.00006536579],"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":[0.0001603436,0.00004493532,0.0007487721,0.00008723984,0.00005285624,0.0001176693,0.000102586,0.8866812,0.009442047,0.03736181,0.000810828,0.06438965],"study_design_scores_gemma":[0.000007255786,0.00001033646,0.00003530094,0.000003145235,0.000003648929,0.00001100215,0.000002627077,0.9906063,0.001130842,0.007639518,0.0005454307,0.000004691694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003769154,0.00005268066,0.9951907,0.00003804852,0.00001685938,0.00002031687,0.00001297373,0.000212022,0.0006872345],"genre_scores_gemma":[0.1885278,0.000169614,0.8091167,0.00008938398,0.00004752606,0.0001764228,0.0001192901,0.000311075,0.001442239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004232998,"threshold_uncertainty_score":0.01037383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733209186880539,"score_gpt":0.2664328277881131,"score_spread":0.2391007359193077,"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."}}