{"id":"W4200492843","doi":"10.1016/j.jfoodeng.2021.110916","title":"Application of tribo-electrostatic separation (T-ES) technique for fractionation of plant-based food ingredients","year":2021,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Ministry of Agriculture - Saskatchewan; University of Saskatchewan","keywords":"Fractionation; Biochemical engineering; Separation method; Pace; Process engineering; Population; Corn starch; Nanotechnology; Environmental science; Starch; Materials science; Chemistry; Engineering; Food science; Geography","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":[0.00029314,0.00006242379,0.000135625,0.0001049634,0.0000174333,0.000006460415,0.0000782297,0.00004177557,0.000004851261],"category_scores_gemma":[0.00008705421,0.00006315854,0.00006859138,0.0001856799,0.000008646052,0.0001375614,0.00001277265,0.00007280588,2.273059e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007976239,"about_ca_system_score_gemma":0.00001210358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000170237,"about_ca_topic_score_gemma":0.000002006851,"domain_scores_codex":[0.9992723,0.00001205503,0.0003575418,0.00007316659,0.0002084474,0.00007648599],"domain_scores_gemma":[0.9994338,0.00006642213,0.0003452471,0.00008035066,0.00005249627,0.0000216964],"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":[0.00003669447,0.0001002592,0.0008885183,0.0001196908,0.00003955797,4.06664e-7,0.00004169996,0.136392,0.8571388,0.0003876355,0.0001706293,0.004684159],"study_design_scores_gemma":[0.0003168074,0.0006646405,0.001533341,0.00007669513,0.00002753539,0.000007000801,0.00001371731,0.04641273,0.9492116,0.0004805668,0.001193046,0.00006232465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1713693,0.00002872344,0.8282277,0.00004695503,0.00004883044,0.0001983254,0.00001111915,0.0000125776,0.0000564828],"genre_scores_gemma":[0.9588977,0.00001224017,0.04101045,0.000008332387,0.00001999054,0.00002557812,0.0000133479,0.000008128727,0.000004209744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7875284,"threshold_uncertainty_score":0.2575531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00765564570317996,"score_gpt":0.2378648138218879,"score_spread":0.230209168118708,"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."}}