{"id":"W4403376773","doi":"10.1021/acsmeasuresciau.4c00047","title":"Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics","year":2024,"lang":"en","type":"article","venue":"ACS Measurement Science Au","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Normalization (sociology); Metabolomics; Database normalization; Computer science; Sample size determination; Data mining; Artificial intelligence; Statistics; Pattern recognition (psychology); Mathematics; Bioinformatics; 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":[0.002583345,0.0001020205,0.0001161713,0.0001634584,0.0001578902,0.00004760372,0.000252967,0.00003740469,0.000007625683],"category_scores_gemma":[0.0005170576,0.00007359948,0.00004551726,0.0007933096,0.00023032,0.00001883375,0.0001238335,0.00005752037,0.000003238548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001208848,"about_ca_system_score_gemma":0.0004371683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001351626,"about_ca_topic_score_gemma":0.000555873,"domain_scores_codex":[0.9987559,0.0000517113,0.0002245209,0.0002904114,0.0004298083,0.0002476272],"domain_scores_gemma":[0.9992449,0.00001734886,0.00005707764,0.0002270686,0.0004230402,0.00003056707],"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":[0.00001359608,0.00003437853,0.002804544,0.00002177496,0.00002139416,2.048401e-7,0.0003142612,0.00006506159,0.9917322,0.003578716,0.00009279801,0.001321116],"study_design_scores_gemma":[0.0002148922,0.0001418066,0.03407924,0.00004445334,0.00003866541,0.000002519389,0.0003900782,0.0005261058,0.9534067,0.000652272,0.01034239,0.0001608824],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858341,0.006783443,0.005017208,0.000378961,0.0005283466,0.000210195,0.00001442008,0.000009830807,0.001223463],"genre_scores_gemma":[0.9989301,0.0004139618,0.0004252988,0.00008761077,0.00009024949,0.00001234437,0.00001637968,0.000007652196,0.00001639668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03832546,"threshold_uncertainty_score":0.30013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03349963247821618,"score_gpt":0.2884695653419059,"score_spread":0.2549699328636897,"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."}}