{"id":"W2620794769","doi":"10.1002/cjce.22910","title":"Grade efficiency for sieve classification processes","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Sieve (category theory); Sensitivity (control systems); Process (computing); Work (physics); Computer science; Particle (ecology); Sieve analysis; Process engineering; Mathematics; Engineering; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002250412,0.0004986751,0.0005698427,0.001467341,0.0002430917,0.001104285,0.0007358256,0.000604893,0.00126502],"category_scores_gemma":[0.005863687,0.0002307878,0.0007891621,0.001119714,0.0004702754,0.0007920287,0.0004002874,0.0004704635,0.000410595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132302,"about_ca_system_score_gemma":0.000241746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004462368,"about_ca_topic_score_gemma":0.002438698,"domain_scores_codex":[0.9982613,0.0003085806,0.0001043319,0.0002314303,0.0009579217,0.0001363792],"domain_scores_gemma":[0.9964147,0.002233231,0.0003548479,0.0004300348,0.0005320197,0.00003524988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009380036,0.0002375712,0.01819895,0.0004038663,0.0000807682,0.0001183991,0.0001494082,0.7181948,0.1813483,0.002024697,0.0003422977,0.07796279],"study_design_scores_gemma":[0.00001421236,0.0006648627,0.0260672,0.00002861081,0.00004539731,0.00006596601,0.00005719192,0.6447363,0.3259884,0.001022886,0.00126334,0.00004563683],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9082208,0.0005670136,0.08640076,0.00005422106,0.00001463951,0.0001299364,0.0004851345,0.000452835,0.003674621],"genre_scores_gemma":[0.9950973,0.0001042819,0.004052457,0.000007435593,9.594969e-7,0.00001548786,0.0001660652,0.00002575011,0.0005302082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004462368,"threshold_uncertainty_score":0.0119015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655517328627294,"score_gpt":0.2309550574779662,"score_spread":0.2043998841916932,"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."}}