{"id":"W4391756946","doi":"10.1007/s42461-024-00924-4","title":"Deep Neural Network Models for Improving Truck Productivity Prediction in Open-pit Mines","year":2024,"lang":"en","type":"article","venue":"Mining Metallurgy & Exploration","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Truck; Artificial neural network; Productivity; Open-pit mining; Profitability index; Computer science; Variable (mathematics); Sensitivity (control systems); Artificial intelligence; Engineering; Machine learning; Data mining; Automotive engineering; Mathematics; Mining engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803828,0.0007677177,0.0006345985,0.0006588434,0.0002634801,0.0005986503,0.001148677,0.001001811,0.001622301],"category_scores_gemma":[0.001261982,0.0004202951,0.0006286513,0.0008166038,0.000224685,0.0008751638,0.0006052456,0.001074606,0.0005588218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006990983,"about_ca_system_score_gemma":0.0006464007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02404001,"about_ca_topic_score_gemma":0.03069103,"domain_scores_codex":[0.9998568,0.00001933396,0.000007027919,0.00004980422,0.00002387016,0.00004311713],"domain_scores_gemma":[0.9995785,0.0001871916,0.00004815535,0.00002981915,0.0001199953,0.00003630875],"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":[0.0002798618,0.0002812303,0.01703784,0.00006026598,0.0001006643,0.0001364578,0.00003381383,0.8840914,0.001690773,0.0006847366,0.004489854,0.09111319],"study_design_scores_gemma":[0.000002415751,0.000006424052,0.0006862554,0.000002156863,0.000003512089,0.00000269121,0.000004456351,0.9988297,0.0001339781,0.0002645084,0.00006252923,0.000001409903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8685527,0.00177675,0.1207752,0.0008515956,0.0001963513,0.0000331919,0.002433303,0.001302523,0.004078428],"genre_scores_gemma":[0.9874017,0.0002222895,0.007568826,0.00006398154,0.00004280966,0.00001595882,0.001595472,0.00003297916,0.003055971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02404001,"threshold_uncertainty_score":0.04780018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04063058700739029,"score_gpt":0.2407281088094115,"score_spread":0.2000975218020212,"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."}}