{"id":"W4405643255","doi":"10.26434/chemrxiv-2024-g7h8g","title":"Enhanced Characterization of Lignin Nanoparticles by Asymmetric Flow-Field Flow Fractionation","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Diffusion Coefficients in Liquids","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universität für Bodenkultur Wien","keywords":"Characterization (materials science); Lignin; Flow (mathematics); Nanoparticle; Fractionation; Field (mathematics); Nanotechnology; Field flow fractionation; Materials science; Chemistry; Chemical engineering; Chromatography; Organic chemistry; Mechanics; Engineering; Physics; Mathematics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002213313,0.0003224292,0.0003831745,0.0002321118,0.00006281885,0.00006936095,0.0003913519,0.0006963333,0.00125039],"category_scores_gemma":[0.0005422616,0.0003437355,0.0001738839,0.0004641422,0.00004914777,0.00007248868,0.0007316892,0.0007837918,0.0001727057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628134,"about_ca_system_score_gemma":0.0001363607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007495033,"about_ca_topic_score_gemma":5.458514e-7,"domain_scores_codex":[0.997833,0.00002699666,0.0006798594,0.0006511061,0.0005434718,0.0002655445],"domain_scores_gemma":[0.9984429,0.000205649,0.0003897822,0.0006117451,0.0002640375,0.00008587672],"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":[0.00002594492,0.0002020353,0.00002782015,0.000870385,0.00007320754,0.000001261462,0.0002717261,0.0000910158,0.9716694,0.00005112089,0.006369568,0.02034655],"study_design_scores_gemma":[0.0002259257,0.00001648854,0.0000367624,0.0004560519,0.00007966663,6.065363e-7,0.00003711392,0.01213927,0.9827296,0.0003908374,0.003591101,0.0002965427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722188,0.0002322918,0.00272331,0.0003065327,0.001753006,0.0001740143,0.00009078478,0.0002556284,0.02224562],"genre_scores_gemma":[0.9922322,0.0001424443,0.0005947893,0.0001000503,0.0004503484,0.0001096419,0.0009511188,0.00006967645,0.005349723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02005001,"threshold_uncertainty_score":0.9999015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009160134042646251,"score_gpt":0.2498128338147613,"score_spread":0.2406526997721151,"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."}}