{"id":"W2888504409","doi":"10.1190/geo2017-0824.1","title":"Uncertainty quantification for inverse problems with weak partial-differential-equation constraints","year":2018,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Posterior probability; Uncertainty quantification; Inverse problem; Applied mathematics; Mathematics; Markov chain Monte Carlo; Mathematical optimization; Covariance; Gaussian; Bayesian probability; Partial differential equation; Prior probability; Computer science; Algorithm; Statistics; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001381473,0.0001061922,0.0001051272,0.00003915749,0.0002078082,0.00005466631,0.0001113016,0.00004317883,0.0003398005],"category_scores_gemma":[0.00002052458,0.00008204282,0.00003512311,0.0001381444,0.0003214935,0.0001868655,0.000004079797,0.00005753412,0.000226693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005282372,"about_ca_system_score_gemma":0.00005617023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439535,"about_ca_topic_score_gemma":0.000117258,"domain_scores_codex":[0.9992359,0.00003110599,0.0001379431,0.0002310258,0.0001546553,0.0002093286],"domain_scores_gemma":[0.9994584,0.00005502642,0.0001052892,0.0001778621,0.0001479695,0.00005541665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004747376,0.00016547,0.02805393,0.0001784701,0.0001226953,0.000001363914,0.002253803,0.002813267,0.005582927,0.01110594,0.04537566,0.9038717],"study_design_scores_gemma":[0.000962996,0.0009011013,0.01120397,0.00008838293,0.00006966313,0.000004495361,0.0005178725,0.9020646,0.02014731,0.01524893,0.04832152,0.0004691147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6748679,0.00001394657,0.3171635,0.0008495866,0.0006190589,0.0008913694,0.0001720005,0.000339463,0.005083102],"genre_scores_gemma":[0.995929,0.00000666912,0.00273305,0.0003808248,0.0002164642,0.000007993423,0.0004436938,0.000004260229,0.0002780408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9034026,"threshold_uncertainty_score":0.3720577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330699774230627,"score_gpt":0.2323359891639741,"score_spread":0.1990289914216678,"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."}}