{"id":"W1604522291","doi":"","title":"Nonlinear geoacoustic inversion via parallel tempering","year":2012,"lang":"en","type":"article","venue":"Canadian acoustics","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":"Tempering; Reverberation; Seabed; Acoustics; Inversion (geology); Parallel tempering; Attenuation; Geology; Nonlinear system; Inverse transform sampling; Algorithm; Bayesian probability; Computer science; Seismology; Materials science; Physics; Optics; Markov chain Monte Carlo; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008427431,0.0005444164,0.0006080689,0.0002781725,0.0003495467,0.0005255432,0.0007491051,0.0004338748,0.002758781],"category_scores_gemma":[0.002472103,0.0004356935,0.0005167745,0.0003828163,0.0006426941,0.0006884302,0.0009006076,0.0008187243,0.0005781689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004387947,"about_ca_system_score_gemma":0.0009157455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004270118,"about_ca_topic_score_gemma":0.004368156,"domain_scores_codex":[0.999718,0.000109494,0.00001328818,0.00003879165,0.0000953368,0.00002503834],"domain_scores_gemma":[0.9994746,0.000218612,0.00004858179,0.000117112,0.000117314,0.00002368745],"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":[0.0001275082,0.00003424269,0.0007734941,0.00004797366,0.00004420945,0.00006438329,0.00006138916,0.8997338,0.01382852,0.01327304,0.0006806531,0.07133073],"study_design_scores_gemma":[0.000003448633,0.000006198092,0.00003670447,8.090442e-7,0.000001346702,0.000006097649,0.000001704591,0.9973153,0.001270181,0.001124823,0.0002311014,0.000002143054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01314934,0.00002507436,0.9851758,0.000037112,0.00001577395,0.00002051866,0.00002295337,0.0004289733,0.001124441],"genre_scores_gemma":[0.3031255,0.00006013315,0.6936218,0.00005526185,0.00002812085,0.0001080675,0.0001642251,0.0002756142,0.002561205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004270118,"threshold_uncertainty_score":0.009229004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395095980069127,"score_gpt":0.2284542791663633,"score_spread":0.204503319365672,"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."}}