{"id":"W2123741511","doi":"10.1002/jssc.200900221","title":"Design and optimization of porous polymer enzymatic digestors for proteomics","year":2009,"lang":"en","type":"article","venue":"Journal of Separation Science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Chromatography; Chemistry; Trypsin; Monolith; Mass spectrometry; Immobilized enzyme; Glycidyl methacrylate; Protease; Proteolytic enzymes; Polymer; Enzyme; Biochemistry; Organic chemistry; Polymerization","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":[0.0003815853,0.00005195881,0.0001044321,0.00007624271,0.00009974617,0.0000307563,0.0001422504,0.0000308484,0.000006230508],"category_scores_gemma":[0.00007639967,0.0000446979,0.0000210947,0.0001662054,0.00009222957,0.0003914586,0.000007408616,0.00005131955,9.036158e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003921715,"about_ca_system_score_gemma":0.0001041508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.51751e-7,"about_ca_topic_score_gemma":2.996467e-8,"domain_scores_codex":[0.9993494,0.000004236023,0.0003160146,0.00008416251,0.0001664991,0.00007968598],"domain_scores_gemma":[0.9990374,0.00003990208,0.0005094542,0.00009102004,0.0002761492,0.00004606545],"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":[0.0000348569,0.00003061758,0.0000138103,0.000008588219,0.000001568379,1.255871e-7,0.0001813798,0.04432203,0.9522598,0.001469938,0.00002290308,0.001654403],"study_design_scores_gemma":[0.0001705121,0.0001245316,0.00001863904,0.00002641423,0.000008726471,0.00002688025,0.00002928122,0.04343646,0.9523221,0.003762783,0.00002276619,0.00005093479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09791776,0.00006703267,0.9015161,0.0002099837,0.000009649249,0.0001646908,0.00000126638,0.000007159043,0.0001063728],"genre_scores_gemma":[0.5093541,0.00003263097,0.4905236,0.00002301755,0.00001812807,0.000006852547,4.258339e-7,0.00000224708,0.00003900355],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4114363,"threshold_uncertainty_score":0.1822727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02497457149466465,"score_gpt":0.3331765425180793,"score_spread":0.3082019710234146,"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."}}