{"id":"W4386525495","doi":"10.56952/arma-2023-0334","title":"Assessment of Empirical Methods-Based Pillar Strength Estimation Through Numerical Modelling","year":2023,"lang":"en","type":"article","venue":"","topic":"Geotechnical and Geomechanical Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pillar; Brittleness; Context (archaeology); Geotechnical engineering; Structural engineering; Computer science; Factor of safety; Numerical models; Stability (learning theory); Geology; Civil engineering; Engineering; Computer simulation; Materials science; Simulation; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003139382,0.0006532161,0.0004841651,0.002111685,0.0002527331,0.001279244,0.001120363,0.001400538,0.001337077],"category_scores_gemma":[0.01000953,0.0003729435,0.0005121607,0.0008029355,0.0007219528,0.001013641,0.0009027494,0.0004743334,0.0003531471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007489224,"about_ca_system_score_gemma":0.0006673764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001930927,"about_ca_topic_score_gemma":0.001396301,"domain_scores_codex":[0.9985659,0.0005704763,0.0001136047,0.0001464135,0.0005488692,0.00005480535],"domain_scores_gemma":[0.9927631,0.004601181,0.0007505533,0.0007871851,0.001019468,0.00007852374],"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":[0.00004909948,0.00007080824,0.006910251,0.0001168629,0.00002672575,0.00005695345,0.00008598641,0.9448789,0.008840174,0.003465723,0.0001457836,0.03535276],"study_design_scores_gemma":[0.000002300241,0.00001970237,0.0005511217,0.00001260887,0.000003282823,0.00001362092,0.00001132901,0.9965665,0.002163007,0.0004362522,0.0002135158,0.000006589863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2954799,0.0004357936,0.6966231,0.0001670455,0.00003185284,0.0001737834,0.0002336023,0.0005486588,0.006306232],"genre_scores_gemma":[0.8715205,0.0001810017,0.1274985,0.00001662612,0.000007484002,0.0001529531,0.0001163082,0.00004361807,0.0004630029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003139382,"threshold_uncertainty_score":0.01660281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04755738959781185,"score_gpt":0.3652203358850894,"score_spread":0.3176629462872775,"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."}}