{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003907024,0.0001140795,0.0001422764,0.000209858,0.0002004168,0.0004211257,0.0002933067,0.00004308276,0.000002232659],"category_scores_gemma":[0.0001190687,0.0001064324,0.0000188675,0.0002796409,0.00002917049,0.000463855,0.0001135975,0.00008139385,2.863897e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004726502,"about_ca_system_score_gemma":0.00005359568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004487851,"about_ca_topic_score_gemma":0.0003397621,"domain_scores_codex":[0.9992091,0.00001595454,0.0002314261,0.0002504437,0.00008798175,0.0002050894],"domain_scores_gemma":[0.9996113,0.0002245619,0.00004960277,0.00006040594,0.00002838679,0.00002569724],"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":[0.00003981074,0.00003184674,0.03480391,0.0005628939,0.00003428174,0.000003199294,0.006958556,0.5173295,0.02261461,0.007792064,0.0008370583,0.4089922],"study_design_scores_gemma":[0.0002467471,0.00002522833,0.003211788,0.0002412664,0.000004718241,0.000001111131,0.0004545321,0.9929993,0.00009530631,0.002467056,0.000133197,0.000119763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1415117,0.00009268954,0.8568256,0.000150272,0.0006811252,0.0001652601,0.000002579585,0.00006056893,0.0005101998],"genre_scores_gemma":[0.9203517,0.000001705408,0.07941721,0.00006160238,0.00006782135,0.00002196433,0.000005442342,0.000005495829,0.00006702605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7788401,"threshold_uncertainty_score":0.4340186,"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."}}