{"id":"W4387788892","doi":"10.1007/978-3-031-38141-6_134","title":"Improving Sustainability in Mining Operations Through the Integration of ShovelSense® and BeltSense® Technologies for Mine-to-Mill Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"MineSense Technologies (Canada)","funders":"","keywords":"Excavator; Profitability index; Sustainability; Truck; Exploit; Mill; Mining industry; Engineering; Emerging technologies; Computer science; Mining engineering; Business; Civil engineering; Automotive engineering; Computer security; Mechanical 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":[0.0002283418,0.0002195655,0.0002639206,0.0002187621,0.00009712894,0.00005657558,0.0001035278,0.0002518977,0.000003522318],"category_scores_gemma":[0.0004908366,0.0001653709,0.00004405902,0.000119572,0.00006799085,0.0001291677,0.00006583046,0.0001818582,4.656486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133651,"about_ca_system_score_gemma":0.0000360713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001266374,"about_ca_topic_score_gemma":0.001075345,"domain_scores_codex":[0.999083,0.000005827369,0.0003946942,0.0002477919,0.00009304482,0.0001756714],"domain_scores_gemma":[0.9993595,0.0001971741,0.0000484388,0.0002068174,0.0001737214,0.00001434395],"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":[0.000008986744,0.000003744876,0.000006778031,0.0004975316,0.00002184355,0.000001110601,0.001865159,0.9463223,0.000715451,0.02298713,0.0003535874,0.02721639],"study_design_scores_gemma":[0.0002728518,0.00008749494,0.00001373826,0.0004665149,0.00004975436,0.000004365404,0.005822839,0.9818059,0.001187278,0.009040856,0.0008141484,0.0004342202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718944,0.0008808881,0.9197104,0.003214403,0.0005241951,0.003504096,0.0001339547,0.001818086,0.05302451],"genre_scores_gemma":[0.6476546,0.0003970385,0.1715618,0.00007591247,0.0001734207,0.0003801327,0.0002330121,0.0002900274,0.1792341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7481486,"threshold_uncertainty_score":0.6743631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516589503023432,"score_gpt":0.2575457487535237,"score_spread":0.2323798537232894,"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."}}