{"id":"W2323508436","doi":"10.1021/acs.analchem.6b00043","title":"Screening Glycolipids Against Proteins in Vitro Using Picodiscs and Catch-and-Release Electrospray Ionization-Mass Spectrometry","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network; Alberta Glycomics Centre; University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates - Technology Futures","keywords":"Chemistry; Electrospray ionization; Chromatography; Mass spectrometry; Electrospray mass spectrometry; Electrospray; Glycolipid; Extractive electrospray ionization; Protein mass spectrometry; In vitro; Sample preparation in mass spectrometry; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371002,0.0002749289,0.0003319528,0.00008505218,0.0001325143,0.00007738877,0.00020004,0.0002450101,0.0007882909],"category_scores_gemma":[0.0002258071,0.0002318757,0.00007481162,0.0005208464,0.0001892483,0.0001238937,0.0001143153,0.0003355848,0.000002635978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156385,"about_ca_system_score_gemma":0.00006611266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003841393,"about_ca_topic_score_gemma":0.000002064683,"domain_scores_codex":[0.9982355,0.00001127305,0.0003959255,0.0005993482,0.0002517443,0.0005062575],"domain_scores_gemma":[0.9990136,0.0001311623,0.0001174527,0.0004171598,0.00005165053,0.0002689717],"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":[0.00003495301,0.00007932162,0.006223768,0.0001044806,0.0000276065,0.00001161589,0.000006651267,0.000002204182,0.9908096,0.001076841,0.00002755811,0.001595367],"study_design_scores_gemma":[0.0006269459,0.00001273134,0.0002906948,0.0001279464,0.00004842386,0.00002621027,0.00004132311,0.007330248,0.9883875,0.002182201,0.0005329118,0.000392834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501118,0.0001890081,0.03061914,0.001035157,0.000005769522,0.000157101,0.00005499273,0.0001902719,0.01763677],"genre_scores_gemma":[0.9867367,0.0001343775,0.01177469,0.00006486943,0.0001886306,0.00004195929,0.00002937748,0.00003925339,0.0009902035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03662485,"threshold_uncertainty_score":0.9455618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151502230912965,"score_gpt":0.2540743747237006,"score_spread":0.242559352414571,"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."}}