{"id":"W4200425433","doi":"10.1101/2021.12.23.474041","title":"MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Metabolomics; Principal component analysis; Database normalization; Computer science; Pattern recognition (psychology); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006253065,0.002117808,0.001472542,0.003190573,0.001318517,0.002274764,0.002068523,0.001301318,0.004516151],"category_scores_gemma":[0.0136834,0.0008325537,0.001451145,0.002173749,0.0009770943,0.001534893,0.001636459,0.002079629,0.002027137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001989953,"about_ca_system_score_gemma":0.001549561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004373001,"about_ca_topic_score_gemma":0.006382654,"domain_scores_codex":[0.9966763,0.0005127732,0.0002421483,0.001111642,0.001267316,0.0001898577],"domain_scores_gemma":[0.9971451,0.0009941777,0.0004019088,0.0004644406,0.0009216306,0.00007271207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001222663,0.000296877,0.01980704,0.001252211,0.0007320156,0.0004814191,0.0006951237,0.02435002,0.2904775,0.007824969,0.02380732,0.6290527],"study_design_scores_gemma":[0.0001107424,0.0003546837,0.03807446,0.0001962438,0.0002163876,0.0008375662,0.0001789814,0.4596723,0.4323189,0.008176273,0.05939692,0.0004665302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03372437,0.001230729,0.9387818,0.0002476197,0.00022311,0.0004277942,0.001837769,0.02118003,0.002346698],"genre_scores_gemma":[0.1067617,0.0004343281,0.8821615,0.0002724607,0.00006430094,0.001568276,0.003210087,0.002814367,0.002713043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006253065,"threshold_uncertainty_score":0.03306973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177607624700409,"score_gpt":0.232843850143765,"score_spread":0.211067773896761,"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."}}