{"id":"W2019926089","doi":"10.1016/j.memsci.2011.07.035","title":"Membrane Dephlegmation: A hybrid membrane separation process for efficient ethanol recovery","year":2011,"lang":"en","type":"article","venue":"Journal of Membrane Science","topic":"Membrane Separation and Gas Transport","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Membrane; Process (computing); Ethanol; Separation (statistics); Membrane technology; Separation process; Chemistry; Chromatography; Chemical engineering; Process engineering; Computer science; Engineering; Organic chemistry; Biochemistry; Machine learning","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":[0.0001455093,0.0003337828,0.0003981588,0.0002320589,0.0003167593,0.0003419828,0.000514683,0.0005618714,0.0008528819],"category_scores_gemma":[0.0001067381,0.0001941021,0.0002711327,0.0002037636,0.0002596408,0.0007390685,0.0005998419,0.0006754639,0.0006159971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002966552,"about_ca_system_score_gemma":0.0002376228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004823506,"about_ca_topic_score_gemma":0.0008874772,"domain_scores_codex":[0.9998677,0.00001191496,0.000007439158,0.0000356698,0.00005411636,0.00002318738],"domain_scores_gemma":[0.9999528,0.000009254927,0.000009020437,0.000009892603,0.0000107858,0.000008321577],"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":[0.00004470144,0.00002016597,0.00004824081,0.00004774713,0.000004861687,0.000030347,0.00001269733,0.0001117082,0.9957595,0.0001275477,0.00006261523,0.003729731],"study_design_scores_gemma":[0.000006304626,0.00003379733,0.0002184211,9.698398e-7,0.00000501821,0.00005862455,0.000003686874,0.0009911682,0.9974353,0.00003222566,0.001210083,0.000004470292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9345184,0.001563974,0.05993631,0.0003532571,0.00009668634,0.00008010436,0.000201238,0.0003959432,0.0028541],"genre_scores_gemma":[0.9702664,0.0008102027,0.02235613,0.0001446793,0.00003127171,0.00004909086,0.0002137642,0.00005805915,0.006070466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008528819,"threshold_uncertainty_score":0.002853155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098502133316336,"score_gpt":0.2820580859160794,"score_spread":0.251073064582916,"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."}}