{"id":"W3170170135","doi":"10.1139/cgj-2020-0569","title":"2019 Canadian Geotechnical Colloquium: Mitigating a fatal flaw in modern geomechanics: understanding uncertainty, applying model calibration, and defying the hubris in numerical modelling","year":2021,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Geomechanics; Calibration; Field (mathematics); Computer science; Uncertainty quantification; Hubris; Software; Earthquake engineering; Construction engineering; Civil engineering; Risk analysis (engineering); Geotechnical engineering; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001114754,0.0004229762,0.0005968655,0.0008454965,0.000502067,0.000394213,0.0004938361,0.0005906292,0.00002896867],"category_scores_gemma":[0.0002390572,0.0004239856,0.00019659,0.001424494,0.00008870057,0.0003205793,0.00009641355,0.002718678,0.000002853241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00288698,"about_ca_system_score_gemma":0.001326287,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1387816,"about_ca_topic_score_gemma":0.2515317,"domain_scores_codex":[0.9965292,0.0001081908,0.0009700644,0.0005223462,0.0004239191,0.001446347],"domain_scores_gemma":[0.9978406,0.0001961296,0.00007641438,0.0004207149,0.00007841794,0.001387667],"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":[0.000003213428,0.000008289981,0.00003592804,0.0000214803,0.00003392779,0.0004028952,0.0001364991,0.9958299,0.0002890888,0.001995412,0.0001581328,0.001085232],"study_design_scores_gemma":[0.000393468,0.00001360736,0.00001403064,0.0002575344,0.00003742552,0.000436034,0.0005079986,0.9842244,0.00003768375,0.01332037,0.0002632593,0.0004941596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01971181,0.001165844,0.9763123,0.002093068,0.000123679,0.0002968863,0.00005991952,0.0001510687,0.0000853911],"genre_scores_gemma":[0.9966084,0.0003587005,0.002394193,0.000369223,0.00009810652,0.00002775346,0.00003229903,0.00008976708,0.00002159104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9768965,"threshold_uncertainty_score":0.9998212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199515281540158,"score_gpt":0.201396100010473,"score_spread":0.1814445718564572,"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."}}