{"id":"W6892787827","doi":"10.5281/zenodo.1212532","title":"Mesh Adaptation In Bayesian Inversion: Combined A Posteriori Control Of Discretisation And Sampling Errors","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"A priori and a posteriori; Bayesian probability; Discretization; Sampling (signal processing); Control (management); Adaptation (eye); Bayesian inference","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003020745,0.0006510892,0.0009676905,0.0005481126,0.0004416184,0.00121301,0.001561714,0.001760153,0.002179107],"category_scores_gemma":[0.01628515,0.0008429889,0.0006349625,0.0006142998,0.001220862,0.001593081,0.002024919,0.001798095,0.0004022311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000700805,"about_ca_system_score_gemma":0.00113932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005981492,"about_ca_topic_score_gemma":0.006880525,"domain_scores_codex":[0.9989232,0.0005360838,0.00005999277,0.0001434799,0.0002770294,0.00006011154],"domain_scores_gemma":[0.9942704,0.004206007,0.0002795159,0.0004927069,0.0006268551,0.0001245224],"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":[0.000381152,0.00009212661,0.0008499499,0.0002237314,0.0001076721,0.00005030677,0.0002356667,0.7940373,0.01572076,0.03952506,0.001838939,0.1469374],"study_design_scores_gemma":[0.00001034116,0.00001075482,0.00008663522,0.000007232209,0.000004642176,0.000007493482,0.00000432653,0.9940436,0.001322782,0.004021423,0.0004748758,0.000005934433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004512122,0.0001351647,0.9945039,0.00009485042,0.00003375178,0.00001318537,0.00001898456,0.0001475881,0.0005405164],"genre_scores_gemma":[0.3008436,0.0003818953,0.6945029,0.0001739682,0.0001276222,0.0001611886,0.0001693669,0.0006048442,0.003034574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005981492,"threshold_uncertainty_score":0.01597542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04184799745169076,"score_gpt":0.2610302499690793,"score_spread":0.2191822525173885,"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."}}