{"id":"W3108122377","doi":"10.1002/jms.4690","title":"Chemical profiling and separation of bioactive secondary metabolites in Maca (<scp><i>Lepidium peruvianum</i></scp>) by normal and reverse phase thin layer chromatography coupled to desorption electrospray ionization‐mass spectrometry","year":2021,"lang":"en","type":"article","venue":"Journal of Mass Spectrometry","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Chemistry; Brassicaceae; Glucosinolate; Chromatography; Nutraceutical; Electrospray ionization; Mass spectrometry; High-performance liquid chromatography; Thin-layer chromatography; Botany; Food science; Brassica","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004547293,0.0002553273,0.0005070805,0.0003857371,0.00005296268,0.00005631009,0.0001458984,0.0002135357,0.00002729813],"category_scores_gemma":[0.0003475047,0.0002602678,0.0001456193,0.0006662196,0.0001093929,0.00005267033,0.00006939693,0.0003841,9.300213e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006506658,"about_ca_system_score_gemma":0.0001602896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005249845,"about_ca_topic_score_gemma":0.000003125992,"domain_scores_codex":[0.9982646,0.000127633,0.0006299661,0.000398707,0.0002404575,0.0003386742],"domain_scores_gemma":[0.9987329,0.0000821327,0.0004763599,0.0001843726,0.0003318825,0.0001924003],"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":[0.0001897553,0.0002245115,0.009372292,0.00007301473,0.0001941195,0.00001138038,0.000122104,0.00001126876,0.9892461,0.00008283521,0.0004250405,0.00004754552],"study_design_scores_gemma":[0.002097023,0.000411515,0.003158544,0.0000358231,0.00008340527,0.0001105074,0.0005356338,0.00008611071,0.9927334,0.0003551721,0.000242336,0.0001505797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805771,0.005792892,0.0129014,0.0001798515,0.00008573991,0.0001884871,0.00008643194,0.000005205708,0.0001828931],"genre_scores_gemma":[0.9682112,0.001517009,0.02966015,0.0001511432,0.0002378527,0.00000579063,0.0001418897,0.00002748686,0.00004748449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01675875,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006080918965309935,"score_gpt":0.254701440394017,"score_spread":0.2486205214287071,"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."}}