{"id":"W2895123429","doi":"10.1016/j.ejor.2018.09.047","title":"Optimizing a mineral value chain with market uncertainty using benders decomposition","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Benders' decomposition; Value (mathematics); Decomposition; Chain (unit); Mathematical optimization; Computer science; Mathematics; Statistics; Chemistry","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":[0.002768446,0.00008750964,0.0001120946,0.0002274023,0.0001848326,0.0001434595,0.0002005047,0.00001785215,0.0002364542],"category_scores_gemma":[0.00004070501,0.00007420501,0.00003644995,0.0001345728,0.00009654627,0.0002382655,0.00004011943,0.0003219389,0.00001197788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826475,"about_ca_system_score_gemma":0.00008760147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005635262,"about_ca_topic_score_gemma":0.000003795553,"domain_scores_codex":[0.998836,0.0002564839,0.0002884775,0.00009440912,0.0002974113,0.0002272429],"domain_scores_gemma":[0.9993002,0.00006715272,0.0000435896,0.0001015946,0.0003804548,0.0001069631],"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":[0.0003831827,0.00004457234,0.0003228177,0.00002873883,0.0001402153,0.00020964,0.001457822,0.9474315,0.02451746,0.001441991,0.0216074,0.002414659],"study_design_scores_gemma":[0.0005634812,0.0007155692,0.0009269635,0.0001897064,0.000007788833,0.0003978502,0.0002405761,0.9908773,0.001681489,0.00006833756,0.004171074,0.0001598485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8950261,0.00006798039,0.07698722,0.0002212408,0.0001227281,0.00009336454,0.000004638576,0.00002880639,0.0274479],"genre_scores_gemma":[0.8899806,0.00002736368,0.1092356,0.00003936973,0.0005948376,6.525638e-7,0.000003095565,0.0000329813,0.00008553357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04344582,"threshold_uncertainty_score":0.3025993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07163220766877489,"score_gpt":0.3370308822454136,"score_spread":0.2653986745766387,"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."}}