{"id":"W4392356356","doi":"10.1080/17480930.2024.2323325","title":"Shovel allocation and scheduling for open-pit mining using deep reinforcement learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Mining Reclamation and Environment","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Shovel; Haulage; Engineering; Reinforcement learning; Open-pit mining; Production (economics); Crusher; Computer science; Operations research; Artificial intelligence; Mining engineering","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":[0.0004758975,0.0000942722,0.0001199177,0.0001431235,0.00005447111,0.0002481169,0.0001222116,0.00004895469,0.00004163636],"category_scores_gemma":[0.00003388077,0.00009701659,0.00003263295,0.0000185416,0.00001659791,0.0003777986,0.00006785546,0.00009514936,0.000001065429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001798152,"about_ca_system_score_gemma":0.00001326565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002083531,"about_ca_topic_score_gemma":6.30988e-7,"domain_scores_codex":[0.9993,0.000008784376,0.0003810741,0.0001034585,0.0001165252,0.0000901628],"domain_scores_gemma":[0.999698,0.00008298901,0.000108992,0.0000394554,0.0000232149,0.00004733636],"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":[0.00003149339,0.000008315388,0.0004416861,0.00006377605,0.0001773642,0.000004762312,0.002976534,0.8817413,0.005714842,0.000975277,0.0001149987,0.1077497],"study_design_scores_gemma":[0.0003016959,0.00007161905,0.000182244,0.0002985512,0.00002856073,0.00007858451,0.0007695923,0.9910229,0.001127795,0.0001972191,0.005811904,0.0001093137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.579459,0.000699033,0.4189475,0.0002289189,0.0003442318,0.00008677157,8.608463e-7,0.00002438065,0.0002093596],"genre_scores_gemma":[0.8928651,0.001236304,0.1056225,0.00003285381,0.0001498694,0.000007442711,0.000008448959,0.00001974858,0.00005779455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3134061,"threshold_uncertainty_score":0.3956221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02887794595235237,"score_gpt":0.2671552734152045,"score_spread":0.2382773274628521,"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."}}