{"id":"W4383497729","doi":"10.1016/j.jcoa.2023.100091","title":"Quantification of caffeoylquinic acids and triterpenes as targeted bioactive compounds of Centella Asiatica in extracts and formulations by liquid chromatography mass spectrometry","year":2023,"lang":"en","type":"article","venue":"Journal of Chromatography Open","topic":"Medicinal Plants and Neuroprotection","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Complementary and Integrative Health; National Institutes of Health; University of British Columbia; Genome British Columbia; Oregon State University; Genome Canada","keywords":"Centella; Terpene; Chromatography; Chemistry; Mass spectrometry; Liquid chromatography–mass spectrometry; Traditional medicine; Organic chemistry; Medicine","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":[0.0007534017,0.0001582453,0.0006815556,0.001617651,0.00005700318,0.00003370458,0.0001466784,0.0001016704,0.00003031659],"category_scores_gemma":[0.0001366228,0.0001283249,0.0001281879,0.001663483,0.0001738828,0.0003472094,0.00004065207,0.0002336009,7.293915e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001478615,"about_ca_system_score_gemma":0.00007405762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001111593,"about_ca_topic_score_gemma":0.000008121602,"domain_scores_codex":[0.998246,0.0001079801,0.0008679261,0.0001895525,0.0004067036,0.0001818045],"domain_scores_gemma":[0.9984847,0.0002217785,0.0007979535,0.000167444,0.0001700988,0.000157987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002100677,0.0003444728,0.05200377,0.0003680114,0.0002680203,0.00004507024,0.000464947,0.000001613822,0.943267,0.0001702599,0.0004154484,0.000550742],"study_design_scores_gemma":[0.004814139,0.006279748,0.7021297,0.00135764,0.0002641789,0.0006966939,0.001169401,0.0002476097,0.2816643,0.0007232577,0.0004650668,0.0001883362],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969398,0.001066517,0.0002173272,0.0004799185,0.0000868148,0.0006040222,0.00003246888,0.00001077525,0.0005623765],"genre_scores_gemma":[0.9967943,0.002211601,0.0008770468,0.00003151605,0.00002768239,0.000005517293,0.0000254678,0.0000173442,0.000009540486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6616027,"threshold_uncertainty_score":0.5232939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261761934078009,"score_gpt":0.3101225118204557,"score_spread":0.2839463184126548,"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."}}