{"id":"W1972959460","doi":"10.1007/s00216-014-8200-2","title":"Quantitative characterization by asymmetrical flow field-flow fractionation of IgG thermal aggregation with and without polymer protective agents","year":2014,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Field-Flow Fractionation Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Characterization (materials science); Flow (mathematics); Polymer; Chemistry; Fractionation; Chromatography; Analytical Chemistry (journal); Materials science; Nanotechnology; Organic chemistry; Mechanics; Physics","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":[0.0001284164,0.0001739121,0.0002396107,0.00007831979,0.00005419468,0.00003872935,0.00005660891,0.0002086223,0.0001223993],"category_scores_gemma":[0.0002417356,0.000143964,0.00003920827,0.000289255,0.0001265887,0.0002136389,0.00002084076,0.0002447063,0.000002427067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003604223,"about_ca_system_score_gemma":0.00001186418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001694478,"about_ca_topic_score_gemma":0.000001425194,"domain_scores_codex":[0.999016,0.00002692744,0.0002677567,0.0002545546,0.0002814773,0.0001532783],"domain_scores_gemma":[0.9993706,0.0001922802,0.00007201978,0.0001127504,0.0001387392,0.0001136547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001366775,0.0008969754,0.04174565,0.00136269,0.001616226,0.000007089568,0.0003708897,0.0007913033,0.6743469,0.006777847,0.002876438,0.2678412],"study_design_scores_gemma":[0.0003463117,0.0001561806,0.004167825,0.00004344393,0.00008138048,0.000006778664,0.00002233229,0.7110584,0.2833181,0.0001874991,0.0004136993,0.0001980481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6021077,0.0001005746,0.3912162,0.001581055,0.0000371582,0.0002678111,0.00004805753,0.000205203,0.004436205],"genre_scores_gemma":[0.9977108,0.00003975606,0.001784888,0.0001055224,0.00005021322,0.00001756445,0.00008611871,0.00001700428,0.0001881701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7102671,"threshold_uncertainty_score":0.587068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007318227622961071,"score_gpt":0.2273038590673436,"score_spread":0.2199856314443825,"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."}}