{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002106784,0.0001177139,0.0002896365,0.00005719985,0.00003796361,0.00001752525,0.0002531242,0.0001168704,0.00007981405],"category_scores_gemma":[0.00008965569,0.000112017,0.0001364759,0.0002884321,0.00008610165,0.00007105792,0.00001992582,0.0002808655,0.000003008251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001345536,"about_ca_system_score_gemma":0.0001824603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001220466,"about_ca_topic_score_gemma":0.000001821683,"domain_scores_codex":[0.9984422,0.00001089517,0.0008534568,0.0001593963,0.0003748546,0.0001592109],"domain_scores_gemma":[0.9985617,0.000108863,0.0005332032,0.0001378199,0.0005704461,0.00008799769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005683066,0.0001423389,0.0002797616,0.0000993369,0.00004820223,0.00000113504,0.00003998665,0.1967857,0.8022652,0.0001076052,0.00007828748,0.0000956224],"study_design_scores_gemma":[0.0003411974,0.00001305795,0.000008048447,0.000109249,0.00003180023,0.00004361059,0.0001069778,0.6137421,0.3792344,0.006158104,0.0001132618,0.00009822856],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602315,0.0001591548,0.03739857,0.0001334814,0.00001378916,0.00002174204,0.00000494386,0.000009110005,0.002027684],"genre_scores_gemma":[0.9982455,0.00001430125,0.001289408,0.0000140631,0.0003163725,0.00000370412,0.00004817286,0.0000105374,0.0000579531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4230308,"threshold_uncertainty_score":0.4567919,"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."}}