{"id":"W938460144","doi":"10.1016/j.jfca.2015.06.006","title":"Profiling gangliosides from milk products and other biological membranes using LC/MS","year":2015,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Alberta","keywords":"Ganglioside; Chemistry; Membrane; Chromatography; Mass spectrometry; Colostrum; Liquid chromatography–mass spectrometry; Selected reaction monitoring; Tandem mass spectrometry; Biochemistry; Biology; Immunology; Antibody","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":[0.0002555065,0.00007940044,0.0001837974,0.0001526712,0.00005656428,0.00005272119,0.00005739769,0.00006734396,0.000009912633],"category_scores_gemma":[0.00005524842,0.0000584699,0.0000745611,0.0002078839,0.00005592049,0.000008759262,0.00003671559,0.00007267988,4.046528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007428645,"about_ca_system_score_gemma":0.00003451843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001747033,"about_ca_topic_score_gemma":0.00000689778,"domain_scores_codex":[0.9993008,0.0001025039,0.000208116,0.000145168,0.0001526161,0.00009073869],"domain_scores_gemma":[0.9994171,0.000009722881,0.0001333432,0.0000830177,0.0002411368,0.0001156961],"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":[0.000192158,0.00005257896,0.01815329,0.000008485031,0.0005073355,0.000005620383,0.00005459318,0.0002379259,0.9802868,0.0000224448,0.00002450715,0.0004543017],"study_design_scores_gemma":[0.001148192,0.0008345599,0.003736031,0.00002764866,0.0003562905,0.0001101493,0.0002928224,0.003330227,0.9881855,0.0002366101,0.00156118,0.0001808385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944338,0.003189098,0.002007015,0.0002195266,0.00002321342,0.00005319877,0.00001446981,0.000002505293,0.00005721871],"genre_scores_gemma":[0.995655,0.0001140175,0.003901318,0.0001152836,0.0001745268,7.897247e-7,0.00002301566,0.000004729372,0.00001133395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01441726,"threshold_uncertainty_score":0.2384333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.056782261218307,"score_gpt":0.3038908521263076,"score_spread":0.2471085909080006,"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."}}