{"id":"W1985483074","doi":"10.5402/2013/450948","title":"Simultaneous Extraction Optimization and Analysis of Flavonoids from the Flowers of <i>Tabernaemontana heyneana</i> by High Performance Liquid Chromatography Coupled to Diode Array Detector and Electron Spray Ionization/Mass Spectrometry","year":2012,"lang":"en","type":"article","venue":"ISRN Biotechnology","topic":"Natural product bioactivities and synthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Central Drug Research Institute","keywords":"Mass spectrometry; Chromatography; Extraction (chemistry); Chromatography detector; Detector; Analytical Chemistry (journal); Ionization; High-performance liquid chromatography; Materials science; Chemistry; Physics; Optics; Organic chemistry; Ion","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001378751,0.0002563296,0.0002159165,0.0003010535,0.0002240967,0.0002424672,0.0001188794,0.0001937519,0.0004746214],"category_scores_gemma":[0.00009951807,0.000109268,0.0003081106,0.0001683819,0.0001480707,0.0002209518,0.0001222575,0.0002967001,0.0001519352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002219861,"about_ca_system_score_gemma":0.0002753429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001791441,"about_ca_topic_score_gemma":0.004393202,"domain_scores_codex":[0.9999295,0.000009684934,0.000003870194,0.00002597088,0.00002160592,0.000009416361],"domain_scores_gemma":[0.9999614,0.000008322658,0.000007979455,0.000003855816,0.00001287231,0.000005624108],"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":[0.00003737072,0.00001589055,0.0002564282,0.00002695861,0.000005805694,0.0000285357,0.00001311261,0.00004108185,0.9966828,0.00001892691,0.00001514043,0.002857835],"study_design_scores_gemma":[0.00003222146,0.0005114435,0.03676249,0.00001595898,0.00006999901,0.0004664173,0.00005574892,0.00256991,0.9532703,0.00009964741,0.006116413,0.0000294195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874148,0.001926551,0.008385155,0.0000870279,0.00001552471,0.00006128278,0.0002071587,0.00007829917,0.001824289],"genre_scores_gemma":[0.9658386,0.001266333,0.02678135,0.0001355116,0.00001308443,0.00006200802,0.0008552774,0.00004520133,0.005002572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001791441,"threshold_uncertainty_score":0.003561974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002589018396895749,"score_gpt":0.2011831992461192,"score_spread":0.1985941808492235,"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."}}