{"id":"W2068556922","doi":"10.1016/j.powtec.2010.09.023","title":"Time delay neural network modeling for particle size in SAG mills","year":2010,"lang":"en","type":"article","venue":"Powder Technology","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grinding; Mill; Artificial neural network; Particle size; Variable (mathematics); Particle (ecology); Engineering; Control theory (sociology); Computer science; Process engineering; Control engineering; Control (management); Artificial intelligence; Mechanical engineering; Mathematics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001188973,0.0001114509,0.0001526438,0.00006936832,0.00005350301,0.00001567846,0.0001520474,0.0002133883,0.0000311305],"category_scores_gemma":[0.00009492497,0.0001119127,0.00002806407,0.0002757247,0.00003756125,0.00006575334,0.00003014438,0.0003386002,0.00002963996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001386739,"about_ca_system_score_gemma":0.000005936646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005104706,"about_ca_topic_score_gemma":0.00005137134,"domain_scores_codex":[0.9991897,0.000003845711,0.000181008,0.0001570753,0.00004079638,0.0004275832],"domain_scores_gemma":[0.9997064,0.00005773355,0.0000146953,0.0001694562,0.00001873678,0.0000329448],"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":[0.000009135941,0.00001759035,0.002110162,0.00003550113,0.00001411193,0.000007110172,0.000114533,0.857583,0.1214574,0.0007711241,0.0008240738,0.01705623],"study_design_scores_gemma":[0.0002811633,0.00001660625,0.00003197127,0.0000137629,0.000005418973,0.00001482051,0.00001857853,0.9907014,0.003418455,0.004385979,0.0009684274,0.000143449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957246,0.0003444095,0.002173785,0.0005453804,0.0002569433,0.000111456,0.000001692762,0.0006237453,0.0002180247],"genre_scores_gemma":[0.9939615,0.000004882756,0.005665019,0.00005539758,0.0001176762,0.00004656666,0.000001774495,0.00003132196,0.0001159107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1331184,"threshold_uncertainty_score":0.4563667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008390005783558379,"score_gpt":0.2207060376223558,"score_spread":0.2123160318387974,"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."}}