{"id":"W4200381554","doi":"10.1016/j.fbp.2021.11.009","title":"Dehydration mechanisms in electrohydrodynamic drying of plant-based foods","year":2021,"lang":"en","type":"article","venue":"Food and Bioproducts Processing","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Staatssekretariat für Bildung, Forschung und Innovation; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Dehydration; Mass transfer; Osmotic dehydration; Convection; Chemistry; Flux (metallurgy); Water content; Moisture; Heat transfer; Evaporation; Chemical engineering; Materials science; Mechanics; Chromatography; Thermodynamics; Physics; Biochemistry","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":[0.0001123127,0.0000961332,0.0001261585,0.00008748898,0.00004526791,0.00003706847,0.00003248861,0.00005071175,0.000003907223],"category_scores_gemma":[0.00003708115,0.0001003351,0.00001288786,0.0003711038,0.00001473784,0.0001716394,0.000005580724,0.00008556362,5.251682e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002723655,"about_ca_system_score_gemma":0.00007145086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001811696,"about_ca_topic_score_gemma":0.0000750585,"domain_scores_codex":[0.9993343,0.00001605689,0.0002088555,0.0001734938,0.0001032853,0.0001640257],"domain_scores_gemma":[0.9997931,0.00001175111,0.00004094122,0.00007498977,0.00005474978,0.0000244436],"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":[0.000009498454,0.00002015356,0.00005679065,0.0002809951,0.000006029597,0.000001463212,0.0003733435,0.00192622,0.9887979,0.0006414884,0.000006309505,0.007879807],"study_design_scores_gemma":[0.0002843608,0.00009540227,0.0003627928,0.00009597803,0.000007950159,0.00000838729,0.0001017023,0.1163114,0.8807295,0.001865881,0.00001508082,0.000121614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541416,0.001245305,0.04392232,0.0001891994,0.00008552497,0.0001039924,0.000009064865,0.0000961488,0.0002068439],"genre_scores_gemma":[0.9940444,0.00002654576,0.005744001,0.00003542233,0.00002392134,0.00000741607,0.0000943889,0.00001432743,0.000009525547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1143852,"threshold_uncertainty_score":0.4091546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007082827533825892,"score_gpt":0.1958868949860233,"score_spread":0.1888040674521974,"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."}}