{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002974891,0.0001753579,0.0001583988,0.00007655952,0.0001323037,0.00002198977,0.0002802589,0.0005582214,0.000002479312],"category_scores_gemma":[0.0003223476,0.0001719034,0.0000777389,0.0001389821,0.0003502148,0.000005864611,0.0001294258,0.0001145503,0.00001341253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006272241,"about_ca_system_score_gemma":0.0001242219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002885561,"about_ca_topic_score_gemma":0.000003476696,"domain_scores_codex":[0.9987623,0.00004913527,0.0002310767,0.0005058346,0.0001374552,0.0003142177],"domain_scores_gemma":[0.9989708,0.00001272368,0.0001325501,0.0005392883,0.0002414822,0.0001031652],"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":[0.0004434698,0.0001630912,0.03420755,0.00004183015,0.00003369056,0.000003473997,0.000018846,0.000009792782,0.8752395,0.0002944355,0.0002714773,0.08927289],"study_design_scores_gemma":[0.0009486077,0.0003151394,0.001516702,0.00001312263,0.00001212902,0.00001091145,0.00003583573,0.0002821408,0.9448463,0.0001393853,0.05167993,0.0001998311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808193,0.002918848,0.01194396,0.002797532,0.0003573034,0.0009022141,0.0000414896,0.0001809352,0.00003835503],"genre_scores_gemma":[0.9771643,0.00004280204,0.02197461,0.00004850823,0.0001942772,0.000221516,0.000236729,0.00002184804,0.00009542008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08907305,"threshold_uncertainty_score":0.7010019,"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."}}