{"id":"W4415929528","doi":"10.1016/j.cageo.2025.106074","title":"Integrating Variational Auto-Encoders (VAEs) and spatial interpolation for improving rock mass domaining in open pit mines","year":2025,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Interpolation (computer graphics); Rock mass classification; Radial basis function; Artificial neural network; Multivariate interpolation; Autoencoder; Inverse distance weighting; Pattern recognition (psychology); Kriging","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":[0.0007662639,0.0005623499,0.0008180392,0.0005388,0.0003095301,0.0006617315,0.001201496,0.0008606517,0.001715815],"category_scores_gemma":[0.001845137,0.0005188539,0.0006646107,0.0005802786,0.0004202402,0.0009246903,0.001117055,0.0009952963,0.0004907778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003684172,"about_ca_system_score_gemma":0.001198508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01714852,"about_ca_topic_score_gemma":0.02960679,"domain_scores_codex":[0.9996976,0.00006626825,0.00001687774,0.00007531194,0.00009905526,0.00004487823],"domain_scores_gemma":[0.9994259,0.0002591584,0.00003672575,0.00008460946,0.0001603056,0.00003329832],"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":[0.0001863642,0.0001287057,0.001885992,0.00008626703,0.00006376621,0.00004682941,0.00008843745,0.7391046,0.01164938,0.003483623,0.001648673,0.2416274],"study_design_scores_gemma":[0.000002301484,0.00000743374,0.00008297933,0.000001522172,0.000002353958,0.000004303279,0.000004212065,0.9986584,0.0007645836,0.0003165912,0.0001533597,0.000001899856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0644428,0.0003726044,0.9322888,0.000108323,0.00006959165,0.0000242204,0.0001481442,0.001337446,0.001208127],"genre_scores_gemma":[0.6265984,0.0001889168,0.3693222,0.00008534316,0.00003418579,0.00003419603,0.0004968286,0.0002988062,0.002941146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01714852,"threshold_uncertainty_score":0.03409743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150164073670966,"score_gpt":0.2534553837074935,"score_spread":0.2419537429707838,"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."}}