{"id":"W2789409847","doi":"10.1080/17480930.2018.1432009","title":"Stochastic optimisation of long-term block cave scheduling with hang-up and grade uncertainty","year":2018,"lang":"en","type":"article","venue":"International Journal of Mining Reclamation and Environment","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti; Newmont Corporation; Barrick Gold Corporation","keywords":"Hang; Open-pit mining; Block (permutation group theory); Production schedule; Scheduling (production processes); Production planning; Schedule; Computer science; Production (economics); Mathematical optimization; Engineering; Mining engineering; Mathematics","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":[0.0001613051,0.00007571669,0.0001096854,0.0000996665,0.0000234032,0.00002498336,0.00006754277,0.0000371329,0.00003536631],"category_scores_gemma":[0.00001251929,0.00006752836,0.00001814176,0.0000139804,0.00006338623,0.0001100652,0.00002082822,0.00006311855,9.198901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007880411,"about_ca_system_score_gemma":0.000006855897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002283079,"about_ca_topic_score_gemma":0.000002448613,"domain_scores_codex":[0.9994649,0.000007616889,0.0002664952,0.00006560746,0.0001327833,0.00006255539],"domain_scores_gemma":[0.9996679,0.00003474737,0.0001733245,0.00004671134,0.00003476336,0.00004254828],"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":[0.0003547922,0.00009468712,0.02327057,0.0001062794,0.0006895765,0.00001987761,0.01381642,0.8163445,0.01412718,0.0003356888,0.0002022436,0.1306382],"study_design_scores_gemma":[0.004339403,0.00159296,0.1068377,0.001746251,0.0002219878,0.00130986,0.002447567,0.8506348,0.02912727,0.0005988314,0.0004007838,0.0007426264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378021,0.00008454821,0.06174053,0.0001039719,0.0001591052,0.00003951437,0.00000244509,0.00000967809,0.00005813771],"genre_scores_gemma":[0.9822847,0.0002727761,0.01727897,0.00001605194,0.0001149356,0.000001671576,0.000003635798,0.000009668071,0.00001755994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1298956,"threshold_uncertainty_score":0.2753727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550008255106031,"score_gpt":0.2272926738633478,"score_spread":0.2117925913122875,"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."}}