{"id":"W2135818676","doi":"10.1002/btpr.2140","title":"Intrinsic fluorescence‐based <i>at situ</i> soft sensor for monitoring monoclonal antibody aggregation","year":2015,"lang":"en","type":"article","venue":"Biotechnology Progress","topic":"Protein purification and stability","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Chemistry; Fluorescence; Protein aggregation; Monoclonal antibody; Fluorescence spectroscopy; In situ; Biological system; Chromatography; Monomer; Least-squares function approximation; Analytical Chemistry (journal); Biophysics; Antibody; Biochemistry; Computer science; Polymer; Statistics; Biology; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006602977,0.0006512052,0.0004123584,0.0002648182,0.0001522289,0.0005415693,0.0006533157,0.0006651896,0.000550477],"category_scores_gemma":[0.0007060257,0.0002847136,0.0004286837,0.0002739222,0.0003983583,0.0005426619,0.0003452639,0.0008408489,0.0003335833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000492884,"about_ca_system_score_gemma":0.0003113814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004029392,"about_ca_topic_score_gemma":0.0007831566,"domain_scores_codex":[0.999493,0.0001176128,0.00002467036,0.0001406029,0.0001896605,0.00003443032],"domain_scores_gemma":[0.9996253,0.0001334166,0.0001091585,0.00003371337,0.00007996855,0.00001837192],"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":[0.00003805809,0.00002665715,0.0002295858,0.00004972163,0.000006383206,0.00001547082,0.00002267435,0.001730134,0.9920974,0.0001836245,0.00006075046,0.005539507],"study_design_scores_gemma":[0.000003392485,0.0001369097,0.0005067422,0.000002542997,0.000009374575,0.00004468224,0.0000109862,0.032998,0.9657922,0.00007285732,0.0004094637,0.00001300917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5349705,0.0007704958,0.4608942,0.0002076938,0.00004628635,0.00007523886,0.0003168662,0.000988981,0.001729723],"genre_scores_gemma":[0.8376497,0.0006393021,0.1586521,0.0001764424,0.00003139251,0.0001456219,0.0002539109,0.00008224505,0.002369337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006651896,"threshold_uncertainty_score":0.003576159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824709024738864,"score_gpt":0.3055829523662631,"score_spread":0.2773358621188745,"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."}}