{"id":"W2922805334","doi":"10.3390/metabo9030057","title":"MetaboAnalystR 2.0: From Raw Spectra to Biological Insights","year":2019,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":339,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Génome Québec; Genome Canada","keywords":"Workflow; Metabolomics; Computer science; Benchmark (surveying); Raw data; Annotation; Computational biology; Data mining; Bioinformatics; Artificial intelligence; Database; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001705468,0.0002878556,0.0005334211,0.0001323721,0.00007276291,0.00005100675,0.0003587975,0.0001393427,0.0005325792],"category_scores_gemma":[0.0001706827,0.0002162962,0.0002304695,0.0003295338,0.00004886677,0.000005514626,0.0002715563,0.0001098264,0.0005209472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008618686,"about_ca_system_score_gemma":0.00002876367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006446739,"about_ca_topic_score_gemma":0.00002863612,"domain_scores_codex":[0.9982868,0.0001014579,0.0003081323,0.0007186761,0.000188037,0.0003968739],"domain_scores_gemma":[0.9989722,0.00003696345,0.00008293704,0.0006553712,0.00008530621,0.0001672607],"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":[0.00006129709,0.0000552982,0.01815208,0.000003097334,0.0003322162,0.000001782187,0.00004611381,0.00001813446,0.9750811,0.004753478,0.0009110803,0.0005843318],"study_design_scores_gemma":[0.0003993421,0.0001514179,0.05934703,0.000002972177,0.00005775323,0.000001391483,0.00008771467,0.00001151346,0.5363786,0.001251004,0.4020048,0.0003065378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725279,0.0192772,0.001219858,0.0002310298,0.000504361,0.0002894565,0.00003856422,0.00002961451,0.005882014],"genre_scores_gemma":[0.9877825,0.001535418,0.007159537,0.0009577451,0.000615736,0.00003070383,0.0001699018,0.00002623062,0.001722227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4387026,"threshold_uncertainty_score":0.8820303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069553652310856,"score_gpt":0.2359653102767074,"score_spread":0.2252697737535988,"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."}}