{"id":"W2606278176","doi":"10.1007/s11783-017-0922-x","title":"Tetra-detector size exclusion chromatography characterization of molecular and solution properties of soluble microbial polysaccharides from an anaerobic membrane bioreactor","year":2017,"lang":"en","type":"article","venue":"Frontiers of Environmental Science & Engineering","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Size-exclusion chromatography; Chemistry; Molecular mass; Membrane; Chromatography; Polysaccharide; Ultrafiltration (renal); Intrinsic viscosity; Gel permeation chromatography; Refractometry; Viscometer; Filtration (mathematics); Analytical Chemistry (journal); Viscosity; Chemical engineering; Materials science; Polymer; Refractive index; Organic chemistry; Biochemistry; Composite material","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000212726,0.0001786359,0.0002671195,0.0001320435,0.0001942018,0.00003810423,0.000626184,0.00008715351,0.00006041315],"category_scores_gemma":[0.00007751457,0.0001714375,0.00005503213,0.0001472965,0.001606659,0.001231514,0.0003882752,0.00007497041,0.000001411705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008855413,"about_ca_system_score_gemma":0.00001086872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004028574,"about_ca_topic_score_gemma":0.000008140718,"domain_scores_codex":[0.9986128,0.0000132778,0.0003306391,0.0003805745,0.0004118235,0.0002509223],"domain_scores_gemma":[0.9990295,0.000006931467,0.0003703146,0.000502434,0.000005569409,0.00008527427],"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":[0.00001950214,0.00006198606,0.0130232,0.00001268994,0.000009547423,5.519379e-7,0.0002294357,0.0002962569,0.985566,0.000007250789,0.000002048589,0.0007715788],"study_design_scores_gemma":[0.0001792113,0.00008473435,0.2116285,0.00003065055,0.00001411493,0.000001017612,0.00006696447,0.00271579,0.7851229,0.000006080533,0.00001563843,0.0001343932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977999,0.0001156656,0.001516166,0.0000292789,0.000203308,0.0002483462,0.00004237195,0.00003446974,0.00001047882],"genre_scores_gemma":[0.9920694,0.0001029648,0.007780326,0.000004447468,0.000008360362,0.000006361698,0.000006580354,0.00001473404,0.000006863414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2004431,"threshold_uncertainty_score":0.6991017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005715825923215136,"score_gpt":0.1822692499829789,"score_spread":0.1765534240597637,"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."}}