{"id":"W2807027360","doi":"10.1002/jcc.25759","title":"Bayesian uncertainty quantification in inverse modeling of electrochemical systems","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Uncertainty quantification; Inverse problem; Bayesian probability; Noise (video); Diffusion; Focus (optics); Inverse; Measurement uncertainty; Experimental data","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006599212,0.001721932,0.001858636,0.002200951,0.0006225716,0.002539892,0.002085683,0.002125302,0.0008277404],"category_scores_gemma":[0.02231106,0.001399358,0.001591554,0.001148207,0.003063979,0.002955784,0.002663577,0.00246289,0.0001744903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001915987,"about_ca_system_score_gemma":0.001426997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005860175,"about_ca_topic_score_gemma":0.003061208,"domain_scores_codex":[0.9969381,0.001432711,0.0001625596,0.0004557648,0.0008803632,0.0001304978],"domain_scores_gemma":[0.9866545,0.01094703,0.001122999,0.000416628,0.0007232888,0.0001355968],"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":[0.00002100052,0.00001327279,0.0003210069,0.0000998785,0.00005847704,0.00005091186,0.00006547113,0.9324621,0.0006641944,0.05748012,0.0001771229,0.008586446],"study_design_scores_gemma":[0.000003421157,0.000007739701,0.00009194529,0.00001524896,0.000007588091,0.00001240455,0.000006139294,0.959282,0.0002929493,0.03998413,0.0002832557,0.00001327153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003835791,0.0003481281,0.9949154,0.0001549162,0.00001167287,0.00001286124,0.0000427274,0.00004739036,0.0006312551],"genre_scores_gemma":[0.6541067,0.002501874,0.3390815,0.0003298449,0.0002453329,0.0003760736,0.0005537575,0.0001724104,0.002632567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006599212,"threshold_uncertainty_score":0.03490043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374026841622623,"score_gpt":0.2600955816462277,"score_spread":0.2463553132300015,"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."}}