{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04873605,0.001799636,0.002263126,0.002055496,0.001639692,0.005382928,0.00294173,0.002352744,0.002120266],"category_scores_gemma":[0.09872609,0.0008202872,0.001462186,0.002969636,0.004982641,0.00625572,0.004428832,0.004226407,0.000901686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002502115,"about_ca_system_score_gemma":0.004687387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00239113,"about_ca_topic_score_gemma":0.003306146,"domain_scores_codex":[0.9724128,0.01276483,0.001392383,0.004973617,0.007943855,0.0005126011],"domain_scores_gemma":[0.9363543,0.04247986,0.004084368,0.009165714,0.007481681,0.0004341118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001305691,0.0002878939,0.02168737,0.006423046,0.001301575,0.0006473938,0.002092537,0.0737088,0.1359269,0.1001635,0.01348922,0.6429659],"study_design_scores_gemma":[0.0001352426,0.0006336184,0.01977791,0.001831444,0.0008208578,0.001135795,0.0007534996,0.3143253,0.200247,0.3587291,0.1011023,0.0005080187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03145047,0.0178681,0.9372345,0.005337227,0.0008069312,0.0001452064,0.0007353971,0.001823077,0.004599093],"genre_scores_gemma":[0.3986863,0.01422436,0.5758779,0.004090854,0.0007707952,0.0007160688,0.002286134,0.001820665,0.001526859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04873605,"threshold_uncertainty_score":0.257744,"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."}}