{"id":"W4312276303","doi":"10.56530/lcgc.na.tn9486s6","title":"Turning Metabolomics Data Processing from a “Black Box” to a “White Box”","year":2022,"lang":"en","type":"article","venue":"LCGC North America","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institutes of Health; University of British Columbia","keywords":"Metabolomics; Feature extraction; Feature selection; Computer science; Black box; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Data mining; Chemistry; Chromatography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001626376,0.000259883,0.000376569,0.00009529861,0.0003858076,0.0000600258,0.000899073,0.00004014161,0.0001960945],"category_scores_gemma":[0.0001712774,0.0002642323,0.0000857641,0.0005281378,0.00008906289,0.000009533203,0.002332149,0.0002365932,0.00002685947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002512194,"about_ca_system_score_gemma":0.0001190971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464006,"about_ca_topic_score_gemma":0.0001543381,"domain_scores_codex":[0.9979721,0.00009284336,0.0003068191,0.0008912836,0.0002737822,0.0004631579],"domain_scores_gemma":[0.9985228,0.00001691016,0.0001987516,0.001044169,0.00005959065,0.0001577884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001791067,0.0009005978,0.1220081,0.00009342493,0.001850108,0.0001054147,0.005049778,0.01375126,0.4453409,0.00006225627,0.1793064,0.2297407],"study_design_scores_gemma":[0.0003806574,0.0002610193,0.005052225,0.000003389841,0.00009611196,0.000005982033,0.001057279,0.0008435233,0.002220076,0.00003125898,0.9896125,0.0004359804],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734639,0.005350273,0.01586115,0.001031906,0.0003595106,0.0003718882,0.00163314,0.00005257229,0.001875692],"genre_scores_gemma":[0.9677066,0.0004880973,0.02358764,0.003569159,0.0005022669,0.00009554092,0.003125231,0.00006147042,0.0008640438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8103061,"threshold_uncertainty_score":0.999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701638394116192,"score_gpt":0.2566565022593248,"score_spread":0.2396401183181629,"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."}}