{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007480847,0.0007703207,0.0007927823,0.0003051371,0.0002939911,0.0005887819,0.001157955,0.0008836207,0.001859561],"category_scores_gemma":[0.002126141,0.0004742224,0.0004316792,0.000248406,0.0007036743,0.0006682048,0.0008522609,0.001298903,0.0001923261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191949,"about_ca_system_score_gemma":0.002003342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695061,"about_ca_topic_score_gemma":0.01757935,"domain_scores_codex":[0.9997457,0.00006344632,0.00001221698,0.00006488033,0.00005152466,0.00006221696],"domain_scores_gemma":[0.9990138,0.0005804136,0.0001225374,0.00004172451,0.0001449184,0.00009661049],"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":[0.00002886963,0.00004434441,0.0006412421,0.00001681321,0.00001060133,0.00003069155,0.00001799037,0.9876254,0.0003290951,0.0006330137,0.0002227856,0.01039908],"study_design_scores_gemma":[0.000003329376,0.000009495161,0.00003891605,0.000001022979,0.000001206415,0.000001515,0.000001937806,0.9995115,0.0000525794,0.0003356764,0.00004188818,9.172538e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1559984,0.0005156028,0.8361657,0.0006776123,0.00007355885,0.0001135303,0.0001385207,0.001197244,0.005119671],"genre_scores_gemma":[0.9578513,0.00007458941,0.04025475,0.0001121,0.00001386178,0.00007047243,0.0001105511,0.00003539405,0.001477025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01695061,"threshold_uncertainty_score":0.03370386,"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."}}