{"id":"W2086472003","doi":"10.1002/anie.201208628","title":"Liquid AP‐UV‐MALDI Enables Stable Ion Yields of Multiply Charged Peptide and Protein Ions for Sensitive Analysis by Mass Spectrometry","year":2013,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Westfälische Wilhelms-Universität Münster; Deutsche Forschungsgemeinschaft","keywords":"Mass spectrometry; Chemistry; Electron-transfer dissociation; Ion; Tandem mass spectrometry; MALDI imaging; Ionization; Dissociation (chemistry); Fragmentation (computing); Analytical Chemistry (journal); Mass spectrum; Collision-induced dissociation; Electrospray ionization; Top-down proteomics; Matrix-assisted laser desorption/ionization; Protein mass spectrometry; Biomolecule; Electrospray; Desorption; Chromatography; Adsorption","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462007,0.001310756,0.0007458403,0.0009112765,0.0005239142,0.001436927,0.0009263879,0.0009689148,0.0069934],"category_scores_gemma":[0.0009035122,0.000552266,0.0004950527,0.0006586096,0.0006759472,0.001579135,0.001939834,0.002297824,0.009123559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003823907,"about_ca_system_score_gemma":0.0004397752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001639109,"about_ca_topic_score_gemma":0.0001743171,"domain_scores_codex":[0.9991991,0.00009708611,0.00004927579,0.0001324376,0.0004553901,0.00006677099],"domain_scores_gemma":[0.99942,0.0001207374,0.00009500193,0.0001165214,0.0001655328,0.0000823037],"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":[0.00005242611,0.00002561368,0.0001043938,0.00006350096,0.000006536926,0.00007798768,0.00001010614,0.00005202349,0.9863704,0.0003431611,0.0004789937,0.01241476],"study_design_scores_gemma":[0.00001152798,0.0001217247,0.0006928311,0.00001910287,0.000009128166,0.0007317238,0.00001222419,0.002068908,0.9851109,0.0005349453,0.01067258,0.00001427949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.219065,0.01395227,0.726005,0.001576172,0.0006284651,0.0006778655,0.002594698,0.01256318,0.02293748],"genre_scores_gemma":[0.5510129,0.01092731,0.4024706,0.001155796,0.000378502,0.0009341815,0.004861906,0.002009278,0.02624939],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0069934,"threshold_uncertainty_score":0.0233953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009266027902802029,"score_gpt":0.2413656512001429,"score_spread":0.2320996232973409,"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."}}