{"id":"W4406299684","doi":"10.1016/j.greeac.2025.100204","title":"An integrated protocol based on workflows of imaged capillary isoelectric focusing (icIEF) for in-depth protein heterogenous characterization: High-efficient fractionation and online mass spectrometry detection","year":2025,"lang":"en","type":"article","venue":"Green Analytical Chemistry","topic":"Protein purification and stability","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Electrophoresis Solutions (Canada)","funders":"FedDev Ontario","keywords":"Isoelectric focusing; Fractionation; Mass spectrometry; Characterization (materials science); Chromatography; Chemistry; Protocol (science); Workflow; Analytical Chemistry (journal); Computer science; Materials science; Nanotechnology; Biochemistry; Database; Medicine","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":[0.00172071,0.001433953,0.0006759595,0.001714925,0.0008931545,0.001076491,0.001298399,0.001237558,0.003918745],"category_scores_gemma":[0.001725145,0.0006827032,0.0007527766,0.0007935801,0.0008709208,0.001167964,0.001465015,0.002580942,0.003691978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008891484,"about_ca_system_score_gemma":0.002804681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008813379,"about_ca_topic_score_gemma":0.001351771,"domain_scores_codex":[0.9980191,0.000273977,0.000214954,0.0005866035,0.00071223,0.0001932009],"domain_scores_gemma":[0.998763,0.0001897073,0.0001686897,0.0003194322,0.0004309509,0.0001282497],"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":[0.0001293262,0.00006196669,0.00026107,0.000216029,0.0000221354,0.0001375833,0.00009398501,0.0002031909,0.9788325,0.001463093,0.001984313,0.01659494],"study_design_scores_gemma":[0.00003267577,0.000157792,0.001382247,0.00003533457,0.00002849167,0.0006137339,0.00003442768,0.002172686,0.9609259,0.0005859782,0.03395322,0.00007752628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06387832,0.001770085,0.9130622,0.0005492643,0.0004249485,0.00372979,0.002838738,0.007362454,0.006384232],"genre_scores_gemma":[0.1024846,0.002813277,0.8612337,0.0007896911,0.0001293883,0.009355862,0.009660658,0.001238915,0.01229396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003918745,"threshold_uncertainty_score":0.01310951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009179031354357693,"score_gpt":0.2740224805959824,"score_spread":0.2648434492416247,"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."}}