{"id":"W1992890348","doi":"10.1039/b812628c","title":"In-capillary enrichment, proteolysis and separation using capillary electrophoresis with discontinuous buffers: application on proteins with moderately acidic and basic isoelectric points","year":2008,"lang":"en","type":"article","venue":"The Analyst","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Chemistry; Chromatography; Capillary electrophoresis; Myoglobin; Isoelectric point; Proteolysis; Isoelectric focusing; Capillary electrophoresis–mass spectrometry; Mass spectrometry; Capillary action; Trypsin; Analytical Chemistry (journal); Biochemistry; Electrospray ionization; Enzyme","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":[0.0001393086,0.0002937346,0.000316171,0.0002455357,0.0003346339,0.00005060066,0.0001273007,0.00007717486,0.000007752787],"category_scores_gemma":[0.000003561433,0.0001975639,0.00003160589,0.0009711828,0.0001505385,0.000198084,0.00001886081,0.0002324241,0.000005976137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001426256,"about_ca_system_score_gemma":0.00005934956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003258337,"about_ca_topic_score_gemma":0.0001328967,"domain_scores_codex":[0.9986789,0.00007424736,0.000256503,0.000397215,0.0002368333,0.0003562864],"domain_scores_gemma":[0.9993523,0.00003334591,0.00008760452,0.0003968804,0.00005124121,0.00007858689],"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":[0.0004923303,0.0001148243,0.002258079,0.00007959444,0.0003571972,0.00003138567,0.0009518088,0.001931733,0.9914255,0.000702672,0.0008892206,0.0007657081],"study_design_scores_gemma":[0.003119357,0.001901529,0.02169393,0.0001712279,0.0009641832,0.001334644,0.0006939009,0.1226956,0.8415977,0.0009191526,0.00296827,0.001940575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734108,0.01298008,0.01190919,0.000266583,0.000003639317,0.001159522,0.000003418878,0.00006693538,0.0001998168],"genre_scores_gemma":[0.9706907,0.02848829,0.0001967774,0.00009363922,0.00004258322,0.0003755068,0.00002115327,0.00004693725,0.00004446838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1498278,"threshold_uncertainty_score":0.8056422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007699262121210412,"score_gpt":0.2065672891695987,"score_spread":0.1988680270483883,"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."}}