{"id":"W4415543253","doi":"10.1007/978-3-031-92870-3_19","title":"Underground Stope Design Under Geological Uncertainty Using Deep Reinforcement Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Quantitative geology and geostatistics","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Profit (economics); Variance (accounting); Set (abstract data type); Engineering design process; Conditional probability; Stage (stratigraphy)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004319201,0.0004779886,0.0006174081,0.0002565283,0.000226202,0.00004114115,0.0001327918,0.0005819292,0.0002473064],"category_scores_gemma":[0.0003300501,0.0004839133,0.00006919149,0.00006387679,0.0003035168,0.0000597398,0.00009045398,0.0008815121,0.0000247545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001270416,"about_ca_system_score_gemma":0.00006736594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002741197,"about_ca_topic_score_gemma":0.00001878198,"domain_scores_codex":[0.9984044,0.0001507904,0.0004974839,0.0003777582,0.0001749339,0.000394669],"domain_scores_gemma":[0.9969945,0.002440387,0.0001188206,0.0002072514,0.0001347547,0.0001042901],"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":[0.0000283563,0.000002182958,0.00001646396,0.00008862619,0.0001853812,0.00002347048,0.00008694217,0.6051882,0.000003779257,0.3936306,0.0001455904,0.0006004349],"study_design_scores_gemma":[0.0003906057,0.0002013407,0.00006830904,0.00008992558,0.0001278389,0.00001263607,0.00008118337,0.8932045,0.000002114384,0.09653156,0.008856051,0.0004339388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003385621,0.002518893,0.9650104,0.00003484887,0.0004130576,0.0002630894,0.00001205396,0.000185548,0.03122353],"genre_scores_gemma":[0.05511699,0.004546943,0.6854567,0.0002343499,0.0001550266,0.00002883095,0.0005671716,0.0001726421,0.2537213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2970991,"threshold_uncertainty_score":0.9997612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06128830584346125,"score_gpt":0.3127324252627422,"score_spread":0.251444119419281,"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."}}