{"id":"W2771993775","doi":"10.3390/membranes7040068","title":"Study of Separation and Fouling of Reverse Osmosis Membranes during Model Hydrolysate Solution Filtration","year":2017,"lang":"en","type":"article","venue":"Membranes","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège Shawinigan; Centre National en Électrochimie et en Technologies Environnementales; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; BioFuelNet Canada","keywords":"Chemistry; Fouling; Nanofiltration; Furfural; Reverse osmosis; Chromatography; Pulp and paper industry; Membrane; Membrane fouling; Biorefinery; Forward osmosis; Sodium hydroxide; Filtration (mathematics); Membrane technology; Chemical engineering; Hemicellulose; Cellulose; Organic chemistry; Biochemistry; Raw material","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":[0.0005306522,0.0004201785,0.0006992102,0.0003113571,0.0003378393,0.0005338783,0.0002668025,0.0006418405,0.0002665569],"category_scores_gemma":[0.0007164699,0.0001601138,0.0006547437,0.0004327334,0.0001980792,0.0005184532,0.0002142931,0.0005455334,0.0001652103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003810314,"about_ca_system_score_gemma":0.0003094654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857077,"about_ca_topic_score_gemma":0.001458596,"domain_scores_codex":[0.9994968,0.00009543232,0.00004157575,0.00009151748,0.0001706608,0.0001040646],"domain_scores_gemma":[0.9996015,0.000137175,0.00009337127,0.00001624097,0.0001232971,0.00002832277],"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":[0.00007482939,0.0000417178,0.000323817,0.00006577391,0.000009560201,0.00004058638,0.00004681462,0.0001881476,0.9978048,0.0000156922,0.00001484256,0.001373415],"study_design_scores_gemma":[0.000004428753,0.0003730999,0.004011351,0.000007296976,0.00002391795,0.00006979992,0.0000597722,0.001120826,0.9939493,0.00001053094,0.0003591679,0.00001060465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975002,0.0006230257,0.00143815,0.0000294996,0.00001064666,0.00002007957,0.0001144281,0.00001434568,0.0002495324],"genre_scores_gemma":[0.9939775,0.001259163,0.003648557,0.00004392111,0.00001163983,0.0000445795,0.0002277629,0.00001865072,0.00076821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001857077,"threshold_uncertainty_score":0.003692567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05107927640167898,"score_gpt":0.336574721151198,"score_spread":0.285495444749519,"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."}}