{"id":"W4404491576","doi":"10.1007/978-3-031-67398-6_295","title":"Post-Melt Purification in the FO–FC–RO Process for Water Recovery from Hydrometallurgical Effluents","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Effluent; Process (computing); Metallurgy; Pulp and paper industry; Environmental science; Materials science; Environmental engineering; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001143408,0.0002959419,0.0003119718,0.0002440033,0.0002525712,0.0004957171,0.0004558464,0.0004785581,0.003934389],"category_scores_gemma":[0.00008685382,0.0001937611,0.0004190217,0.0003807128,0.0002350078,0.0009030681,0.000265334,0.0008155265,0.00213853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576444,"about_ca_system_score_gemma":0.000211929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007389907,"about_ca_topic_score_gemma":0.001906522,"domain_scores_codex":[0.9999143,0.000005485987,0.000002569614,0.00001814539,0.00004676516,0.0000127192],"domain_scores_gemma":[0.9999781,0.000008614706,0.000002468046,0.000003422627,0.000006219188,0.000001148041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001099011,0.0000948145,0.0001081025,0.0005476174,0.00001195551,0.000198608,0.0001900897,0.001647004,0.8121882,0.01270171,0.003786177,0.1684159],"study_design_scores_gemma":[0.000006038837,0.0001791616,0.0007023673,0.00007510048,0.00002137167,0.0004452506,0.00005745493,0.006699276,0.8508589,0.003070849,0.1378651,0.00001892763],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.3539951,0.07328942,0.3243825,0.001877916,0.001487828,0.000227469,0.0008019557,0.002259182,0.2416786],"genre_scores_gemma":[0.4563694,0.02469713,0.07654237,0.0004320381,0.0002516339,0.00009104978,0.0006336991,0.0005755502,0.4404072],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.003934389,"threshold_uncertainty_score":0.01316184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167504798315076,"score_gpt":0.2542565629906621,"score_spread":0.2325815150075113,"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."}}