{"id":"W4378082624","doi":"10.3390/min13060714","title":"Simulation of Solvent Extraction Circuits for the Separation of Rare Earth Elements","year":2023,"lang":"en","type":"article","venue":"Minerals","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Data scrubbing; Process engineering; Extraction (chemistry); Stripping (fiber); Calibration; Electronic circuit; Separation process; Separation (statistics); Computer science; Process (computing); Process simulation; Solvent extraction; Chemistry; Chromatography; Mechanical engineering; Engineering; Mathematics; 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":[0.0001495453,0.00005255884,0.00008000691,0.0000682242,0.00003645572,0.000008993156,0.00003765178,0.00003257623,0.00007781531],"category_scores_gemma":[0.00006659958,0.00004376846,0.00004051457,0.0001897846,0.000007324475,0.000152714,0.000002991972,0.00002765963,0.00001232559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007335496,"about_ca_system_score_gemma":0.000008227651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002169923,"about_ca_topic_score_gemma":0.00001529925,"domain_scores_codex":[0.9994896,0.000008510058,0.0002603421,0.00005785611,0.0001128973,0.00007079826],"domain_scores_gemma":[0.9995121,0.0002105952,0.00007755902,0.0000809709,0.0001066853,0.00001209115],"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":[0.000004349766,0.000008656619,0.00005904701,0.00006985909,0.00001664785,4.891371e-8,0.0001337532,0.7137989,0.2795319,0.0001093632,0.0030566,0.00321081],"study_design_scores_gemma":[0.0002212411,0.00002649217,0.00111951,0.00001364391,0.00001546968,2.494482e-7,0.00009524761,0.8167618,0.1238092,0.0001076094,0.05777755,0.00005204534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6860014,0.0005154265,0.2986214,0.0002673291,0.001162379,0.001268471,0.00009753137,0.0003948964,0.01167113],"genre_scores_gemma":[0.9951491,0.00005054644,0.00009562846,0.00001173102,0.0000545487,0.00004206997,0.00006794751,0.000009167,0.004519224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3091477,"threshold_uncertainty_score":0.1784826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05134438027009327,"score_gpt":0.3450275317224086,"score_spread":0.2936831514523153,"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."}}