{"id":"W2006150742","doi":"10.1016/j.abb.2014.06.014","title":"Improved high sensitivity analysis of polyphenols and their metabolites by nano-liquid chromatography–mass spectrometry","year":2014,"lang":"en","type":"article","venue":"Archives of Biochemistry and Biophysics","topic":"Phytoestrogen effects and research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sciex (Canada); Spinal Cord Injury BC","funders":"National Center for Research Resources; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"Chromatography; Mass spectrometry; Chemistry; Polyphenol; Liquid chromatography–mass spectrometry; High-performance liquid chromatography; Nano-; Materials science; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001446516,0.0001809351,0.0006307279,0.000139679,0.00004967274,0.000009633988,0.00006112739,0.00006339594,0.000003177791],"category_scores_gemma":[0.00004591424,0.0001260774,0.0002219299,0.0004910948,0.0006355938,0.00002930752,0.00007454619,0.0001223379,7.035373e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002565563,"about_ca_system_score_gemma":0.00002611944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007363041,"about_ca_topic_score_gemma":5.205489e-7,"domain_scores_codex":[0.9990963,0.00006029666,0.0001990953,0.000299433,0.0001256881,0.0002191386],"domain_scores_gemma":[0.9990509,0.0003284375,0.000129914,0.0003050787,0.00003718816,0.0001485536],"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":[0.000246934,0.0001916661,0.002332283,0.0004062268,0.001476627,9.816026e-7,0.00006804649,3.095188e-7,0.991937,0.0001423737,0.000003246779,0.003194316],"study_design_scores_gemma":[0.0004940924,0.0004542178,0.009266613,0.00005067279,0.0005766972,0.000004773902,0.00007358759,0.001401606,0.9872573,0.0002914862,0.000009313207,0.0001196128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970615,0.001135757,0.00107,0.00008853064,0.000008838688,0.0001045574,0.0002680066,0.00001260686,0.000250194],"genre_scores_gemma":[0.9981972,0.0003314914,0.001235903,0.00002289299,0.00005874027,0.000002732238,0.0001144158,0.00001000862,0.00002663621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00693433,"threshold_uncertainty_score":0.5141289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003739733840738417,"score_gpt":0.221435208674778,"score_spread":0.2176954748340396,"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."}}