{"id":"W2234266814","doi":"10.1016/j.mineng.2015.10.022","title":"Size-by-size analysis of dry gravity separation using a 3-in. Knelson Concentrator","year":2015,"lang":"en","type":"article","venue":"Minerals Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Concentrator; Gravity separation; Central composite design; Gold ore; Volume (thermodynamics); Specific gravity; Quartz; Materials science; Particle size; Grain size; Gangue; Environmental science; Response surface methodology; Pulp and paper industry; Mineralogy; Chemistry; Metallurgy; Composite material; Engineering; Chromatography; Chemical engineering; Physics; Electrical engineering","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":[0.000262531,0.000220787,0.0004644663,0.0002332227,0.00001772046,0.00003817777,0.0001137929,0.0001064278,0.00001436395],"category_scores_gemma":[0.0002007376,0.0002397989,0.00008533309,0.001431511,0.00001436043,0.0002250296,0.00001851816,0.0001574248,0.000001920563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001495454,"about_ca_system_score_gemma":0.00002391885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001619504,"about_ca_topic_score_gemma":0.00003055992,"domain_scores_codex":[0.9988232,0.00001734472,0.0004176986,0.0001974339,0.0002085841,0.0003357916],"domain_scores_gemma":[0.9994618,0.0001035679,0.00005607022,0.0001732994,0.00006796256,0.0001372926],"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":[0.000003866415,0.00001093043,0.003888394,0.00005662485,0.00009661129,0.000003136502,0.000270962,0.6426653,0.3527296,0.00002181472,0.0001929089,0.000059816],"study_design_scores_gemma":[0.0004120342,0.00001435069,0.001560831,0.00006904163,0.0001604291,0.000001139591,0.0000396058,0.9717881,0.02510357,0.00000727316,0.0005615346,0.0002820824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890672,0.0008749392,0.009254256,0.00000904795,0.0002161859,0.00008315824,0.00002222857,0.0001673231,0.0003056441],"genre_scores_gemma":[0.9962846,0.00001354753,0.003405951,0.000009001827,0.0000730921,0.000008197509,0.00001857431,0.0000349356,0.0001520848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3291228,"threshold_uncertainty_score":0.9778715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847708820600928,"score_gpt":0.2613283543309461,"score_spread":0.2428512661249368,"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."}}