{"id":"W2026796098","doi":"10.1080/01496395.2013.809762","title":"Nanofiltration for the Recovery of Low Molecular Weight Polysaccharides and Polyphenols from Winery Effluents","year":2013,"lang":"en","type":"article","venue":"Separation Science and Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Chemistry; Nanofiltration; Membrane; Polyphenol; Polysaccharide; Chromatography; Permeation; Fractionation; Cellulose; Microfiltration; Biochemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004850273,0.0004206955,0.0003488425,0.0003534474,0.0002868533,0.0002932067,0.0002196262,0.000492666,0.0003630744],"category_scores_gemma":[0.0004887401,0.0001407451,0.0005359187,0.0002453519,0.0001459189,0.0003688269,0.000184422,0.0004211284,0.0001697199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004172008,"about_ca_system_score_gemma":0.0003300303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001398514,"about_ca_topic_score_gemma":0.001936717,"domain_scores_codex":[0.9997725,0.00003924907,0.00001886257,0.00004467354,0.0000959817,0.00002886052],"domain_scores_gemma":[0.999889,0.00003415106,0.0000213847,0.000007002982,0.00003315765,0.00001532262],"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":[0.00003268278,0.00001264692,0.000116349,0.00007095738,0.000007080715,0.00001843009,0.000009923664,0.00006603431,0.9974832,0.00002248721,0.00001449456,0.002145631],"study_design_scores_gemma":[0.00001064062,0.0003210055,0.004549376,0.00001525771,0.00002844188,0.0002717218,0.00002700719,0.0009221821,0.9918393,0.00003420449,0.001970372,0.0000104055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806851,0.003574447,0.01445061,0.00009961209,0.00002090816,0.00005347331,0.0002183315,0.00008759872,0.0008098112],"genre_scores_gemma":[0.9705815,0.003234616,0.0238958,0.00007598545,0.00001398122,0.00005531767,0.0005041258,0.00002990335,0.001608779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001398514,"threshold_uncertainty_score":0.003027022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007194898694868292,"score_gpt":0.2448539926681763,"score_spread":0.237659093973308,"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."}}