{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003176862,0.0007044305,0.0003390707,0.0003030609,0.0001268796,0.0003449924,0.0002771573,0.0003715717,0.0003434657],"category_scores_gemma":[0.0002882701,0.0001634256,0.0002509431,0.0002368294,0.0002070886,0.0002025147,0.0003155651,0.0003818181,0.0002006566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123627,"about_ca_system_score_gemma":0.0001351105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003483742,"about_ca_topic_score_gemma":0.0006772307,"domain_scores_codex":[0.9996632,0.00004244586,0.00002763823,0.00007488451,0.0001556265,0.00003619414],"domain_scores_gemma":[0.9998488,0.00004481581,0.00003550007,0.00001703136,0.00003071038,0.00002298049],"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":[0.00001711221,0.000005859913,0.00008635291,0.0000153666,0.000002693593,0.00001148703,0.000004905736,0.00004677086,0.998881,0.000009920961,0.000007642068,0.0009106652],"study_design_scores_gemma":[0.00000303507,0.0001027979,0.0006668071,0.000002019607,0.000008729809,0.00007842785,0.000007120247,0.0008901982,0.9978172,0.000008119675,0.0004121131,0.000003512527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474628,0.001689656,0.04787634,0.00008882408,0.00003397542,0.0002357331,0.0004934233,0.0003541049,0.001765134],"genre_scores_gemma":[0.9139016,0.002481113,0.07886954,0.000185674,0.0000220294,0.0003014269,0.001150665,0.00007111935,0.003016859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007044305,"threshold_uncertainty_score":0.001680136,"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."}}