{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004003316,0.0009828723,0.0006553761,0.0008981858,0.0004309408,0.0005563388,0.0006595898,0.000512159,0.0006957235],"category_scores_gemma":[0.0006344289,0.0003197183,0.0003372859,0.000665506,0.0003682047,0.0005001675,0.0003241409,0.0007136899,0.0007381415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003540627,"about_ca_system_score_gemma":0.0006257598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008720364,"about_ca_topic_score_gemma":0.001494505,"domain_scores_codex":[0.9996125,0.0000446256,0.00002711045,0.0000833565,0.0001611931,0.00007109815],"domain_scores_gemma":[0.9996408,0.0001047794,0.00006302268,0.00002638925,0.00009447598,0.00007041377],"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":[0.00003286447,0.00001592633,0.00006291126,0.00006586991,0.000005134822,0.00006548684,0.000009171697,0.00003080147,0.9970042,0.00004106864,0.00003104219,0.002635459],"study_design_scores_gemma":[0.00001251978,0.0001188924,0.001940727,0.000008128755,0.0000189744,0.0005096509,0.00001731036,0.0008312772,0.994459,0.00009466767,0.001977731,0.00001100239],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7573617,0.01384336,0.2186399,0.0003916579,0.0001914697,0.0008550166,0.00259526,0.0008689791,0.005252726],"genre_scores_gemma":[0.6935909,0.0102556,0.2829194,0.0004308121,0.0001393059,0.0005331501,0.003994157,0.0002839792,0.007852749],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009828723,"threshold_uncertainty_score":0.00256896,"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."}}