{"id":"W1994047166","doi":"10.1016/j.jcis.2006.11.012","title":"Nature identification and morphology characterization of anion-exchange membrane fouling during conventional electrodialysis","year":2007,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Membrane-based Ion Separation Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Electrodialysis; Fouling; Characterization (materials science); Ion exchange; Chemical engineering; Morphology (biology); Chemistry; Membrane; Membrane fouling; Chromatography; Ion; Materials science; Nanotechnology; Organic chemistry; Biochemistry; Engineering; Biology","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":[0.001143915,0.00007233628,0.0001566825,0.0004313386,0.00007930846,0.00004582551,0.000120653,0.00006880439,0.00003200144],"category_scores_gemma":[0.00007031529,0.00006836807,0.00002821088,0.0004082663,0.0001350186,0.000577732,0.000018664,0.0001742415,4.944901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004972457,"about_ca_system_score_gemma":0.00002973306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.847033e-7,"about_ca_topic_score_gemma":0.000001087867,"domain_scores_codex":[0.9990784,0.00001529876,0.0004271684,0.0001038856,0.0002540105,0.0001212654],"domain_scores_gemma":[0.9992958,0.00002886205,0.0002596361,0.00006888429,0.0002834827,0.0000633805],"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":[0.00003503782,0.000008964621,0.000260542,0.0000497931,0.000008504017,4.162956e-7,0.0001537619,0.00008480061,0.9990425,0.00009541047,0.00001140646,0.0002489139],"study_design_scores_gemma":[0.0002054523,0.00006105464,0.01709036,0.00004626684,0.00001097699,0.00008387142,0.00004132062,0.001738264,0.9804649,0.0000349977,0.0001635525,0.00005899619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623435,0.000341056,0.03683112,0.0000747185,0.0002431512,0.00007103002,0.000003030935,0.00001834641,0.00007407396],"genre_scores_gemma":[0.9986836,0.0005246417,0.0006502193,0.00001710295,0.00005460766,9.062221e-7,0.000001261453,0.0000056922,0.00006197736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03634012,"threshold_uncertainty_score":0.2787969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004595462136957836,"score_gpt":0.2535144836250355,"score_spread":0.2489190214880777,"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."}}