{"id":"W197861672","doi":"10.1021/pr034116g","title":"Liquid Chromatography MALDI MS/MS for Membrane Proteome Analysis","year":2004,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Chromatography; Proteome; Shotgun proteomics; Chemistry; Proteomics; Matrix-assisted laser desorption/ionization; Mass spectrometry; Electrospray ionization; Protein mass spectrometry; Bottom-up proteomics; Tandem mass spectrometry; Sample preparation; Shotgun; Liquid chromatography–mass spectrometry; Membrane protein; Membrane; Biochemistry; Desorption","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002403387,0.0001986817,0.0005389642,0.001618442,0.0003126015,0.0001419417,0.000884797,0.0002100635,0.00353792],"category_scores_gemma":[0.0002628927,0.0001644999,0.0007303423,0.00325342,0.000178003,0.0002023579,0.0001183211,0.000991634,0.0000128553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964387,"about_ca_system_score_gemma":0.0003110238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009262298,"about_ca_topic_score_gemma":0.00001239948,"domain_scores_codex":[0.9969186,0.00005438548,0.0007720734,0.0003306532,0.001239482,0.0006847734],"domain_scores_gemma":[0.9974746,0.0001398106,0.0004145794,0.0006006825,0.00104586,0.0003245001],"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":[0.0003905334,0.0004788925,0.0003275849,0.0004686387,0.0007593487,0.0000312746,0.00007542039,0.00008626239,0.9908694,0.005881908,0.0004434778,0.0001872489],"study_design_scores_gemma":[0.001131691,0.0009505551,0.0002669563,0.0001564387,0.0001829663,0.00007656992,0.00007735852,0.00007215334,0.9625766,0.01461445,0.01965929,0.0002350232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359539,0.0005590736,0.03265508,0.007486686,0.00003293469,0.00222007,0.000104885,0.0001496488,0.02083779],"genre_scores_gemma":[0.9493937,0.0002247807,0.04814176,0.00001660571,0.0005207189,0.0008510723,0.00001225247,0.00004650966,0.0007925791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02829286,"threshold_uncertainty_score":0.997373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04379495627351521,"score_gpt":0.3717784203483823,"score_spread":0.3279834640748671,"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."}}