{"id":"W1504272476","doi":"10.1007/978-3-540-78831-7_57","title":"Automation and Robotics in Mining and Mineral Processing","year":2009,"lang":"en","type":"book-chapter","venue":"Springer handbooks","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Automation; Mineral processing; Engineering; Manufacturing engineering; Process (computing); Robotics; Process control; Production (economics); Productivity; Control (management); Process automation system; Production line; Process engineering; Systems engineering; Robot; Computer science; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001113115,0.0003152371,0.0003279615,0.000267753,0.00006884299,0.0001424782,0.0000588352,0.0002698347,0.000005134843],"category_scores_gemma":[0.000007983394,0.0003360292,0.00002586772,0.00002198418,0.00004695515,0.0001233551,0.00003461826,0.0002888826,0.00000236261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000541482,"about_ca_system_score_gemma":0.00001856602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002525685,"about_ca_topic_score_gemma":0.00004500255,"domain_scores_codex":[0.999056,0.000003715575,0.0002952835,0.0002735457,0.0001295581,0.0002419165],"domain_scores_gemma":[0.9997138,0.0000184334,0.00006557869,0.0001040248,0.00002034829,0.00007780996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001393885,0.000008321103,0.0006130483,0.001983444,0.00004726128,0.00009075335,0.002153965,0.01225513,0.002451853,0.003191625,0.0003726505,0.976818],"study_design_scores_gemma":[0.004361647,0.0003281245,0.006977532,0.03467375,0.000474404,0.0004207268,0.0001521098,0.8700534,0.001073845,0.02598948,0.04903367,0.006461362],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1025644,0.04197298,0.002351527,0.000184358,0.0005231058,0.0005842229,0.00000896256,0.001270811,0.8505397],"genre_scores_gemma":[0.6946794,0.001259113,0.03852043,0.0001466901,0.0009283406,0.00001735459,0.00003464924,0.0003577143,0.2640563],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9703566,"threshold_uncertainty_score":0.9999092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454802312207857,"score_gpt":0.2100655362646736,"score_spread":0.1955175131425951,"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."}}