{"id":"W2089176871","doi":"10.1016/j.aca.2010.11.014","title":"Strategy of using microsome-based metabolite production to facilitate the identification of endogenous metabolites by liquid chromatography mass spectrometry","year":2010,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Genome Alberta; Canada Research Chairs; Genome Canada","keywords":"Metabolite; Chemistry; Microsome; Metabolomics; Chromatography; Metabolic pathway; Mass spectrometry; Liquid chromatography–mass spectrometry; Tandem mass spectrometry; Metabolism; Biochemistry; In vitro","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":[0.0004796277,0.0008184813,0.0005610067,0.0005084029,0.0003779559,0.0005444582,0.0004219896,0.0004082009,0.0005035465],"category_scores_gemma":[0.0004100525,0.000327713,0.0004331077,0.0003155629,0.0003586833,0.0003942833,0.0008528677,0.0009424296,0.0008065765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002927841,"about_ca_system_score_gemma":0.0007164853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006019145,"about_ca_topic_score_gemma":0.001143127,"domain_scores_codex":[0.9996371,0.00007987118,0.00002406686,0.00008633416,0.0001132179,0.00005943282],"domain_scores_gemma":[0.9998003,0.00003909859,0.00002474136,0.00004729229,0.00005550304,0.00003308118],"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":[0.00004111517,0.00002473194,0.0001093707,0.00002042201,0.000006783359,0.00003547826,0.000009068901,0.00005745163,0.9972203,0.0002196334,0.00005749855,0.002198187],"study_design_scores_gemma":[0.000008127831,0.0000976822,0.0006651892,0.000002671174,0.00001272192,0.00009350834,0.000009044511,0.0008201678,0.9961682,0.0001469409,0.001966038,0.000009764406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4069629,0.001824009,0.5787916,0.001242464,0.0003099042,0.001492098,0.00225194,0.001676694,0.005448366],"genre_scores_gemma":[0.7000443,0.001831794,0.2872134,0.0004358674,0.0000867902,0.00100804,0.002645139,0.0002053976,0.006529229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008184813,"threshold_uncertainty_score":0.002536535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02338586621870992,"score_gpt":0.2503036590954543,"score_spread":0.2269177928767444,"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."}}