{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002174042,0.0004564181,0.0005055153,0.0003440326,0.00034453,0.0005255152,0.0007016811,0.0009951167,0.003775508],"category_scores_gemma":[0.0009033625,0.0002144884,0.0006158248,0.0003047257,0.0003291671,0.0002913729,0.0002987478,0.000384139,0.0002363617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006606004,"about_ca_system_score_gemma":0.00087275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008525601,"about_ca_topic_score_gemma":0.004534907,"domain_scores_codex":[0.9998741,0.0000315919,0.000007385651,0.00002139503,0.00003731132,0.00002821457],"domain_scores_gemma":[0.999432,0.0003872737,0.00004832402,0.00002446407,0.00008600249,0.00002183775],"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":[0.00004422186,0.00002252286,0.000638163,0.00003235572,0.000006763616,0.00003116603,0.00002102738,0.9941186,0.002193533,0.0009102976,0.00009070406,0.001890691],"study_design_scores_gemma":[0.000009700262,0.00002510687,0.000122881,0.000003147977,0.000003753251,0.000004895797,0.00000620377,0.9982671,0.001080981,0.0001524907,0.0003212169,0.000002441588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7146252,0.0005227573,0.2542952,0.0003203743,0.00009788674,0.0002373426,0.0009449852,0.001349912,0.02760635],"genre_scores_gemma":[0.9688043,0.0002484572,0.02507064,0.00003483642,0.000007307831,0.0002200424,0.000364339,0.00004645129,0.005203769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008525601,"threshold_uncertainty_score":0.01695198,"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."}}