{"id":"W3107099202","doi":"10.1007/s00603-020-02295-w","title":"A Bayesian Approach for Uncertainty Quantification in Overcoring Stress Estimation","year":2020,"lang":"en","type":"article","venue":"Rock Mechanics and Rock Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stress (linguistics); Reliability (semiconductor); Bayesian probability; Probabilistic logic; Uncertainty quantification; Borehole; Estimation; Computer science; Statistics; Geology; Geotechnical engineering; Mathematics; Engineering","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":[0.007276505,0.001280211,0.002492681,0.002426337,0.001087884,0.002758816,0.003158875,0.002451798,0.002755122],"category_scores_gemma":[0.02712962,0.001880321,0.001969782,0.002182777,0.002304662,0.003645249,0.00342349,0.003262566,0.0004933308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693204,"about_ca_system_score_gemma":0.002437173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01254578,"about_ca_topic_score_gemma":0.01277732,"domain_scores_codex":[0.9966666,0.001455766,0.0002168784,0.0005622893,0.0009115887,0.0001868261],"domain_scores_gemma":[0.9843466,0.01250287,0.0006736484,0.0006672109,0.001560036,0.0002495102],"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":[0.00007469608,0.00006484913,0.0006830546,0.0001791474,0.0001759925,0.00008845569,0.0001492128,0.7900872,0.001563566,0.1238176,0.001404822,0.08171136],"study_design_scores_gemma":[0.000005991985,0.00001527545,0.0001579195,0.00002595294,0.00002020766,0.00002009466,0.00000795364,0.9391458,0.0002971021,0.05957326,0.0007093723,0.00002102794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009602378,0.0001411381,0.99844,0.00007441729,0.00001088417,0.000008703726,0.00002422791,0.00003724654,0.0003031872],"genre_scores_gemma":[0.2186087,0.001238233,0.7735903,0.0003677996,0.0004128075,0.0002671587,0.0004997425,0.0002833346,0.004731878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01254578,"threshold_uncertainty_score":0.03848231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165968532345369,"score_gpt":0.2134433858962558,"score_spread":0.1968465326617189,"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."}}