{"id":"W3173639095","doi":"10.82308/861","title":"Application of efficient frameworks for joint simulation of multi-element mineral deposits and stochastic optimization of open pit mine production scheduling","year":2014,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Stanford Bio-X; AngloGold Ashanti; Newmont Corporation; Barrick Gold Corporation","keywords":"Open-pit mining; Scheduling (production processes); Production (economics); Joint (building); Mining engineering; Computer science; Mineral resource classification; Geology; Mathematical optimization; Engineering; Geochemistry; Mathematics; Civil engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006302559,0.0001571867,0.0003166724,0.0001385781,0.00008804644,0.00001436256,0.0001517475,0.0001767008,0.000003721689],"category_scores_gemma":[0.0003224678,0.0001776636,0.0000517144,0.0001353564,0.00002720831,0.0001883239,0.00009306184,0.000150819,3.325559e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009687121,"about_ca_system_score_gemma":0.000003913764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003379257,"about_ca_topic_score_gemma":0.00002118087,"domain_scores_codex":[0.9988165,0.00002877643,0.0006315787,0.0002683498,0.0001017467,0.0001530783],"domain_scores_gemma":[0.9990696,0.00008518261,0.0003186929,0.0002778765,0.0001967104,0.00005190067],"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":[0.00003675342,0.00007316951,0.00003232407,0.0002511367,0.00002670127,1.593397e-8,0.000009256891,0.9113261,0.05766247,0.002938243,1.573576e-7,0.02764374],"study_design_scores_gemma":[0.0004724024,0.0001132387,0.0001846295,0.0001454915,0.00004336692,9.170668e-7,0.00001482892,0.9010075,0.09742457,0.0004129444,0.00003565355,0.0001444442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6445752,0.00002410854,0.3543771,0.000004785117,0.00007560525,0.0008057687,0.00003982669,0.00004649854,0.00005103959],"genre_scores_gemma":[0.798404,0.000007571104,0.201413,0.000005212294,0.00001272543,0.00008218189,0.00003664678,0.00003367039,0.000004953632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1538288,"threshold_uncertainty_score":0.7244914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193384332734094,"score_gpt":0.2485563075341448,"score_spread":0.2266224642068039,"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."}}