{"id":"W4387299229","doi":"10.1021/acs.nanolett.3c00562","title":"Extracellular Vesicle Refractive Index Derivation Utilizing Orthogonal Characterization","year":2023,"lang":"en","type":"article","venue":"Nano Letters","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Center for Advancing Translational Sciences; NIH Clinical Center; National Cancer Institute; Prostate Cancer Foundation; National Institutes of Health; Multiple Sclerosis Society; University of Ottawa; National Multiple Sclerosis Society","keywords":"Nanoparticle tracking analysis; Refractive index; Optics; Particle (ecology); Mie scattering; Light scattering; Refractometry; Materials science; Nanoparticle; Characterization (materials science); Population; Particle size; Scattering; Nanotechnology; Physics; Chemistry; Microvesicles","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":[0.0002151335,0.0001615401,0.000102577,0.00009747522,0.0001276695,0.00004163436,0.00017615,0.0001299974,0.00003451132],"category_scores_gemma":[0.00009739903,0.0001840082,0.00008492038,0.0002991982,0.00006671338,0.00001993599,0.00009491837,0.00008416676,0.0001233977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433876,"about_ca_system_score_gemma":0.00004322512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003262046,"about_ca_topic_score_gemma":0.00000176023,"domain_scores_codex":[0.9987532,0.00008354553,0.0002259105,0.0004074751,0.0002244499,0.0003053681],"domain_scores_gemma":[0.9993693,0.0000143926,0.0001247821,0.0003530775,0.0000517756,0.00008673209],"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":[0.00006794738,0.00003267334,0.009179987,0.00001452091,0.00002893633,0.00002482656,0.00004983694,0.00008073749,0.9857619,0.0001102511,0.0009768868,0.003671466],"study_design_scores_gemma":[0.0005641566,0.00005019095,0.1015669,0.00002093813,0.00002066794,0.00000924579,0.00009345177,0.0005389478,0.8647445,0.00004034897,0.03201411,0.0003364875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870944,0.0001275984,0.01124363,0.0008613589,0.0002232323,0.0001976543,0.00002287558,0.00008554626,0.0001437081],"genre_scores_gemma":[0.9965895,0.00003957389,0.0002148981,0.0008841667,0.0003190838,0.00003560976,0.001354642,0.0000461151,0.0005164407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1210174,"threshold_uncertainty_score":0.7503638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01140720613745682,"score_gpt":0.2427015264006339,"score_spread":0.231294320263177,"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."}}