{"id":"W2093597634","doi":"10.1121/1.4781454","title":"Bayesian inversion of propagation and reverberation data","year":2006,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Reverberation; Acoustics; Gibbs sampling; Geology; Scattering; Inversion (geology); A priori and a posteriori; Bayesian probability; Maximum a posteriori estimation; Underwater acoustics; Computer science; Mathematics; Statistics; Underwater; Physics; Seismology; Optics; Maximum likelihood; Artificial intelligence","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":[0.001129386,0.0005871673,0.0005673952,0.0008541328,0.0002545847,0.0006315019,0.0007717075,0.0005331993,0.001321424],"category_scores_gemma":[0.00485904,0.000519419,0.0005360447,0.000636472,0.0004842429,0.001376405,0.0008839319,0.0006568866,0.000510998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005092463,"about_ca_system_score_gemma":0.001431366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006687873,"about_ca_topic_score_gemma":0.007022517,"domain_scores_codex":[0.9994003,0.0001600582,0.00002684001,0.0001046867,0.000244412,0.00006372268],"domain_scores_gemma":[0.9990866,0.0004503727,0.0001020438,0.0001148317,0.0002154243,0.00003063212],"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":[0.000146077,0.00008063799,0.002402144,0.00007050872,0.00005857515,0.0001005357,0.00009252335,0.8467264,0.01284045,0.01776785,0.0008097031,0.1189046],"study_design_scores_gemma":[0.00001012897,0.00001331583,0.0006381213,0.000004484752,0.000004995636,0.00001818469,0.000009901761,0.9910677,0.001965579,0.005954955,0.0003013555,0.00001126562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04722157,0.00006581831,0.9508494,0.00006833104,0.00001527849,0.00001884171,0.0001389573,0.0004097638,0.001212069],"genre_scores_gemma":[0.713405,0.0002157032,0.2825776,0.00007645896,0.00003869622,0.0001077687,0.001051636,0.0001841569,0.002343043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006687873,"threshold_uncertainty_score":0.01329792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126834421320608,"score_gpt":0.2496748868714103,"score_spread":0.2284065426582042,"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."}}