{"id":"W2066741087","doi":"10.1016/j.ijbiomac.2014.08.018","title":"Separation of macromolecular proteins and rejection of toxic heavy metal ions by PEI/cSMM blend UF membranes","year":2014,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada; Department of Science and Technology, Republic of South Africa","keywords":"Membrane; Chemistry; Ultrafiltration (renal); Metal ions in aqueous solution; Bovine serum albumin; Macromolecule; Metal; Aqueous solution; Phase inversion; Chromatography; Polymer chemistry; Chemical engineering; Organic chemistry; Biochemistry","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":[0.0004854347,0.0001523368,0.0003064928,0.0001214729,0.00004526056,0.00002406341,0.0004253765,0.0001383009,0.000267879],"category_scores_gemma":[0.0004977057,0.0001138204,0.0001317686,0.0001496629,0.0004548921,0.0001886569,0.0001627732,0.0001604864,0.000008896954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004668548,"about_ca_system_score_gemma":0.00001271304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005575527,"about_ca_topic_score_gemma":0.00001413363,"domain_scores_codex":[0.9983057,0.0001797015,0.0006716644,0.0002119649,0.0004913847,0.0001395898],"domain_scores_gemma":[0.9988887,0.0001151125,0.000684909,0.0001320373,0.0001173788,0.00006186415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001453887,0.0001673803,0.002438946,0.00001037023,0.0000734666,0.000008656499,0.00005247054,0.0002436693,0.9908748,0.0008634354,0.0001710225,0.004950357],"study_design_scores_gemma":[0.000489817,0.0009234035,0.004805646,0.00003787712,0.00002413532,0.0002001099,0.00007798152,0.0004383978,0.9866563,0.004275277,0.001939754,0.0001313226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876388,0.0002648354,0.01002619,0.0007394497,0.0001459227,0.0001533461,0.00002868903,0.00001949222,0.0009833012],"genre_scores_gemma":[0.9959517,0.0003061769,0.003556857,0.00007525621,0.00003383143,0.00000600413,0.00001353589,0.000007334014,0.00004936364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008312867,"threshold_uncertainty_score":0.4641462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197852856166598,"score_gpt":0.2653969035274552,"score_spread":0.2534183749657892,"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."}}