{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901564,0.00009272874,0.0001188289,0.00006621237,0.0001650044,0.0001791214,0.0004327386,0.00005567796,0.000003360764],"category_scores_gemma":[0.0006198584,0.00007071261,0.00004845487,0.00005740353,0.00004449283,0.0001423379,0.000004899325,0.0002064304,0.00000128301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009624239,"about_ca_system_score_gemma":0.000164822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005403788,"about_ca_topic_score_gemma":0.00006000873,"domain_scores_codex":[0.9994422,0.000001677852,0.0001870958,0.00005295651,0.00008692401,0.0002291797],"domain_scores_gemma":[0.9994181,0.00006272865,0.00007885824,0.0001477243,0.00009796175,0.0001946151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001602884,0.00001728293,0.0005552688,0.001513863,0.0001601446,0.00003310343,0.002182283,0.4120996,0.5667172,0.002644107,0.004978432,0.009082621],"study_design_scores_gemma":[0.001027578,0.00006267781,0.000835448,0.0008485905,0.0001179478,0.000341969,0.00005934601,0.5521382,0.4288816,0.001403827,0.01357037,0.0007123909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882181,0.0008928129,0.008726246,0.0008349521,0.0006369182,0.0001019602,0.000007706656,0.00004312613,0.0005381797],"genre_scores_gemma":[0.9989614,0.000003260512,0.0006119662,0.00001120759,0.0003604777,0.000004254907,9.731029e-7,0.00002174878,0.00002472193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1400386,"threshold_uncertainty_score":0.2883576,"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."}}