{"id":"W2022409628","doi":"10.5740/jaoacint.11-142","title":"Determination of Major Phenolic Compounds in <i>Echinacea</i> spp. Raw Materials and Finished Products by High-Performance Liquid Chromatography with Ultraviolet Detection: Single-Laboratory Validation Matrix Extension","year":2011,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Herbal Medicine Research Studies","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"U.S. Food and Drug Administration; National Institutes of Health; British Columbia Ministry of Agriculture and Lands; U.S. Environmental Protection Agency; Office of Dietary Supplements","keywords":"Repeatability; Chlorogenic acid; Chromatography; Chemistry; Echinacea (animal); Raw material; High-performance liquid chromatography; Analyte; Traditional medicine; Organic chemistry","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":[0.002774518,0.001240535,0.0006422395,0.0008695771,0.0008341797,0.001114307,0.0007579669,0.001072048,0.0007497022],"category_scores_gemma":[0.003399348,0.0005101193,0.0009054612,0.0007990558,0.0007164355,0.0006673555,0.0007580142,0.0007474615,0.0005231402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006926737,"about_ca_system_score_gemma":0.00153321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850111,"about_ca_topic_score_gemma":0.003898804,"domain_scores_codex":[0.9962921,0.0007031814,0.0003733368,0.0009660446,0.00146698,0.0001983409],"domain_scores_gemma":[0.9974957,0.0005235866,0.0005996446,0.000289373,0.000978562,0.0001130653],"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":[0.0002284967,0.0001620856,0.002111556,0.0001421322,0.00006641349,0.00006004962,0.00007569852,0.0002576111,0.9881437,0.00005481891,0.00008154949,0.00861584],"study_design_scores_gemma":[0.00005164234,0.001770073,0.02160412,0.00004383782,0.0001431642,0.0005998123,0.00006725803,0.002258525,0.9701076,0.00008080483,0.003230204,0.00004291135],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8857172,0.002324373,0.105132,0.0001755137,0.00009791828,0.00146979,0.001427016,0.001115699,0.002540577],"genre_scores_gemma":[0.7548115,0.002331589,0.2309873,0.0006038678,0.00005978068,0.002646064,0.004204525,0.0002525979,0.004102709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002774518,"threshold_uncertainty_score":0.01467323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577757653296059,"score_gpt":0.2673844168228876,"score_spread":0.251606840289927,"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."}}