{"id":"W2762734462","doi":"10.36487/acg_rep/1710_43_whittier","title":"Robust mine schedule optimisation","year":2017,"lang":"en","type":"article","venue":"","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Excellence in Mining Innovation","funders":"Curtin University of Technology","keywords":"Schedule; Computer science; Flexibility (engineering); Volatility (finance); Process (computing); Time horizon; Asset (computer security); Net present value; Risk analysis (engineering); Identification (biology); Operations research; Production (economics); Engineering; Business; Economics; Finance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00004128726,0.00003523632,0.00004016403,0.00001170928,0.00005231606,0.00005441433,0.0001001184,0.00002901159,0.0003305183],"category_scores_gemma":[0.00000760902,0.00003570364,0.00001261051,0.00000327128,0.000007532725,0.0001044994,0.00001812662,0.00002712641,0.00006219689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344542,"about_ca_system_score_gemma":0.000001459514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001445592,"about_ca_topic_score_gemma":0.00001425733,"domain_scores_codex":[0.9998316,4.879751e-7,0.00005027915,0.00004286143,0.00001206557,0.00006276068],"domain_scores_gemma":[0.9997305,0.000002354062,0.00001078365,0.0002329885,0.000004245877,0.00001913968],"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":[0.000009731974,0.00004503791,0.01434696,0.0001225253,0.00009333076,0.000008366586,0.000316095,0.6204671,0.007882859,0.05217961,0.2289189,0.0756095],"study_design_scores_gemma":[0.0001217907,0.00001162572,0.003003052,0.000008260209,0.000003333595,0.000002434896,0.00001347107,0.9653553,0.01454793,0.0003711671,0.01642287,0.000138795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.628441,0.00001558482,0.05125758,0.0001495651,0.0001843379,0.00004452839,0.000001174203,0.0004730253,0.3194332],"genre_scores_gemma":[0.8805611,0.00002176571,0.1182842,0.00001360302,0.00005397209,0.000003881382,0.000002042008,0.000009257318,0.001050217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3448882,"threshold_uncertainty_score":0.3618944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05051111826041928,"score_gpt":0.2282012835511244,"score_spread":0.1776901652907051,"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."}}