{"id":"W1979473472","doi":"10.1021/ac026340f","title":"Selective Extraction and Characterization of a Histidine-Phosphorylated Peptide Using Immobilized Copper(II) Ion Affinity Chromatography and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry","year":2003,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Plant Biotechnology Institute; University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Chemistry; Phosphorylation; Histidine; Peptide; Chromatography; Mass spectrometry; Threonine; Phosphopeptide; Protein mass spectrometry; Protein phosphorylation; Matrix-assisted laser desorption/ionization; Serine; Tandem mass spectrometry; Desorption; Biochemistry; Amino acid; Protein kinase A; Organic chemistry; Adsorption","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001786883,0.0002521885,0.0004243849,0.0001343889,0.0001576682,0.00003437115,0.00008705937,0.0003028511,0.001458716],"category_scores_gemma":[0.0001424798,0.000268766,0.0001097422,0.001076655,0.0001762608,0.0001356907,0.00003758239,0.0002383013,0.000001095863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430837,"about_ca_system_score_gemma":0.00006242601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002320148,"about_ca_topic_score_gemma":4.332921e-7,"domain_scores_codex":[0.9984938,0.00002942572,0.0005636058,0.0004427141,0.0002404214,0.0002300171],"domain_scores_gemma":[0.9988162,0.00009006717,0.0004324907,0.0002939438,0.000236911,0.0001303868],"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":[0.00006264509,0.0002451331,0.007363509,0.000311228,0.00008561592,0.000001365083,0.00002081484,0.000003920243,0.9913327,0.000516692,0.00002165534,0.00003478034],"study_design_scores_gemma":[0.0005125231,0.00003797208,0.004505557,0.00009584208,0.0001964709,0.00004957465,0.00003769814,0.005583873,0.98818,0.0004491042,0.0001028912,0.0002484605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933356,0.000104325,0.002460702,0.00002627463,0.000008131167,0.0001406865,0.00005872417,0.0000998208,0.003765706],"genre_scores_gemma":[0.994188,0.0001635813,0.004845555,0.000004901968,0.00003002385,0.00001751988,0.0002241889,0.00003056912,0.0004956574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005579953,"threshold_uncertainty_score":0.9999765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031060085048017,"score_gpt":0.2555834608567306,"score_spread":0.2452728600062505,"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."}}