{"id":"W2266854835","doi":"10.1021/acs.jnatprod.5b00338","title":"Glucosinolate Diversity in <i>Bretschneidera sinensis</i> of Chinese Origin","year":2015,"lang":"en","type":"article","venue":"Journal of Natural Products","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"European Regional Development Fund; Canada Foundation for Innovation; Conseil Général de la Marne; Centre National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Enseignement Supérieur et de la Recherche; Conseil Régional Champagne Ardenne","keywords":"Glucosinolate; Bark (sound); High-performance liquid chromatography; Biology; Botany; Chemistry; Stereochemistry; Chromatography; Ecology","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":[0.000105276,0.0001574789,0.0001853362,0.0007526288,0.0002117119,0.000163394,0.0001016386,0.0001241537,0.0005546258],"category_scores_gemma":[0.00009633225,0.00008836188,0.0001831635,0.0004972732,0.0001727487,0.0001412654,0.0001778251,0.000171502,0.0001035433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001926459,"about_ca_system_score_gemma":0.000137702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003563332,"about_ca_topic_score_gemma":0.005624881,"domain_scores_codex":[0.9999415,0.000005621422,0.000007702671,0.00002399541,0.00001177174,0.000009308383],"domain_scores_gemma":[0.9998853,0.00001176702,0.00003967706,0.00001122129,0.00002959225,0.00002246107],"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":[0.0001438761,0.00001788509,0.04195073,0.00004539167,0.0000283296,0.0001145258,0.0002522706,0.00003392667,0.9527048,0.00003646415,0.00001813271,0.004653707],"study_design_scores_gemma":[0.00001176164,0.0001717416,0.964365,0.000004492116,0.00004191445,0.0003756198,0.0001930344,0.0002180277,0.03379358,0.00004283271,0.0007721558,0.000009886543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995216,0.00008326319,0.00006982071,0.000006956319,6.643627e-7,0.000003211394,0.0001216473,0.000003864684,0.0001888411],"genre_scores_gemma":[0.9986203,0.0001164878,0.0002122155,0.00002648665,0.000001974266,0.000007036063,0.0006076664,0.000003777385,0.0004040086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003563332,"threshold_uncertainty_score":0.007085204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02445268204548237,"score_gpt":0.267671667646994,"score_spread":0.2432189856015116,"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."}}