{"id":"W2115722053","doi":"10.1002/rcm.2391","title":"Targeted comparative proteomics by liquid chromatography/matrix‐assisted laser desorption/ionization triple‐quadrupole mass spectrometry","year":2006,"lang":"en","type":"article","venue":"Rapid Communications in Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Marine Biosciences","funders":"Canadian Institute for Theoretical Astrophysics; Genomic Health","keywords":"Chemistry; Chromatography; Mass spectrometry; Sample preparation in mass spectrometry; Selected reaction monitoring; Electrospray ionization; Triple quadrupole mass spectrometer; Protein mass spectrometry; Top-down proteomics; Tandem mass spectrometry; Matrix-assisted laser desorption/ionization; Ion suppression in liquid chromatography–mass spectrometry; Electrospray; Liquid chromatography–mass spectrometry; Surface-enhanced laser desorption/ionization; Analytical Chemistry (journal); Desorption; 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":[0.001206147,0.0009364188,0.0008427202,0.001052628,0.000487607,0.001015363,0.0008098346,0.0008248043,0.001471956],"category_scores_gemma":[0.0008352037,0.0002942796,0.0006575154,0.0005920852,0.0004869688,0.0006545225,0.0005826686,0.0006658102,0.0009443176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194269,"about_ca_system_score_gemma":0.0005015834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000380385,"about_ca_topic_score_gemma":0.0008434364,"domain_scores_codex":[0.9989944,0.0001572307,0.0000593826,0.000310838,0.0004140633,0.00006407058],"domain_scores_gemma":[0.9994716,0.0001701654,0.0001240078,0.00005857976,0.0001258605,0.00004982925],"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":[0.00006789354,0.0000244804,0.0001495935,0.00009878753,0.0000176758,0.00003332533,0.00001196352,0.00005006146,0.9921554,0.00007160429,0.00009022689,0.007229095],"study_design_scores_gemma":[0.00004948184,0.0006803147,0.007615797,0.00001623734,0.000103316,0.001736323,0.00003662263,0.003102977,0.978832,0.0003298939,0.007453064,0.00004404474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.490081,0.01637877,0.480121,0.0008877677,0.0003367867,0.0008481627,0.003615942,0.00334426,0.004386324],"genre_scores_gemma":[0.5016971,0.008439695,0.4803881,0.001022212,0.0002016059,0.00104898,0.003623046,0.0002966624,0.003282554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001471956,"threshold_uncertainty_score":0.00637877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803668483338102,"score_gpt":0.2840349317808623,"score_spread":0.2659982469474813,"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."}}