{"id":"W2069449487","doi":"10.1021/ac060485v","title":"Nonretentive Solid-Phase Extraction of Phosphorylated Peptides from Complex Peptide Mixtures for Detection by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry","year":2006,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Chromatography; Mass spectrometry; Phosphopeptide; Solid phase extraction; Ion suppression in liquid chromatography–mass spectrometry; Sample preparation; Extraction (chemistry); Peptide; Desorption; Matrix-assisted laser desorption/ionization; Polystyrene; Tandem mass spectrometry; Adsorption; Biochemistry; Polymer; Organic chemistry","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":[0.0003874722,0.0009588078,0.0004989576,0.0005306402,0.0003085177,0.0003920205,0.0004324241,0.0004715381,0.001255559],"category_scores_gemma":[0.0003132558,0.0003454193,0.0002651153,0.0004490733,0.000314644,0.0004634523,0.0004412178,0.0009064914,0.001232264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440922,"about_ca_system_score_gemma":0.0005476964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002628782,"about_ca_topic_score_gemma":0.0006544411,"domain_scores_codex":[0.9996007,0.00005958604,0.00002728623,0.00009710836,0.000176636,0.00003871699],"domain_scores_gemma":[0.9998449,0.00005565393,0.00003388273,0.00001636101,0.00002990283,0.00001924051],"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":[0.00001191144,0.000008694481,0.00002458973,0.00004931394,0.00000304835,0.0000202937,0.000005824492,0.00002333907,0.9981882,0.00004705587,0.00002563366,0.001592029],"study_design_scores_gemma":[0.000007292769,0.00007664949,0.001044141,0.000006826439,0.000008429205,0.0001788913,0.000009079711,0.0008231411,0.9942805,0.0001181817,0.003438622,0.000008226236],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5172861,0.01688196,0.4554191,0.0003244096,0.0002601101,0.001035404,0.001941388,0.001587623,0.005263956],"genre_scores_gemma":[0.5916749,0.02011413,0.3726467,0.0004951718,0.000148922,0.0009453741,0.004054455,0.0003524179,0.009567984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001255559,"threshold_uncertainty_score":0.00420028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388534531886432,"score_gpt":0.3145894120180743,"score_spread":0.30070406669921,"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."}}