{"id":"W2597627929","doi":"10.1021/acs.analchem.7b00580","title":"Ultrafast Separation and Analysis of Monoclonal Antibody Aggregates Using Membrane Chromatography","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Chemistry; Chromatography; Monoclonal antibody; Membrane; Resolution (logic); Polyvinylidene fluoride; High-performance liquid chromatography; Analytical Chemistry (journal); Antibody; Artificial intelligence; Biochemistry","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":[0.00005001128,0.0001078729,0.0002439863,0.00005543881,0.0001165462,0.00004702625,0.0001336656,0.00007928262,0.0001112396],"category_scores_gemma":[0.00001340507,0.0001091088,0.0001162317,0.0002175046,0.0001424275,0.00006246696,0.00001951585,0.00007360332,0.000001107912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001256964,"about_ca_system_score_gemma":0.00001059106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002635516,"about_ca_topic_score_gemma":7.763148e-7,"domain_scores_codex":[0.9994134,0.000003319468,0.0001891632,0.0001548346,0.0001006335,0.000138652],"domain_scores_gemma":[0.9994744,0.00002033739,0.00005967606,0.0003348544,0.00003628728,0.00007444407],"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":[0.000003043462,0.00001445837,0.01184141,0.00005391187,0.0006526019,0.000001363638,0.00001335981,0.0001722065,0.9870006,0.0001126651,0.00008536989,0.00004899353],"study_design_scores_gemma":[0.00009864886,0.000003764509,0.01761251,0.00001283437,0.000869721,0.000005597159,0.00001349179,0.153536,0.8274088,0.00005939857,0.0002478646,0.0001313462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942338,0.002209407,0.001326266,0.00002229279,0.000005487314,0.00003057568,0.00002704125,0.00003047743,0.002114686],"genre_scores_gemma":[0.9943148,0.005480021,0.00005694836,0.00000434779,0.00002592838,0.000002074178,0.00007985624,0.000009555041,0.00002641368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1595918,"threshold_uncertainty_score":0.4449328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048327838349679,"score_gpt":0.277089462705059,"score_spread":0.2666061843215622,"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."}}