{"id":"W4391741317","doi":"10.1007/s11306-023-02086-8","title":"Evaluation of normalization strategies for GC-based metabolomics","year":2024,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome Canada; Mitacs; Canada Foundation for Innovation; Genome Alberta; DNA Genotek","keywords":"Normalization (sociology); Metabolomics; Derivatization; Gas chromatography–mass spectrometry; Sample preparation; Chromatography; Chemistry; Mass spectrometry","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":[0.002105199,0.0002166158,0.0003224956,0.0001687519,0.00007633246,0.00007268987,0.0001819364,0.0001525046,0.00003450008],"category_scores_gemma":[0.0003177471,0.0001982698,0.0002318055,0.0002465736,0.00008729575,0.0000160157,0.00005281623,0.00006825952,0.000003877644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002622421,"about_ca_system_score_gemma":0.0005684743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008381483,"about_ca_topic_score_gemma":0.00002966172,"domain_scores_codex":[0.9984438,0.0001263319,0.0003978956,0.0004151897,0.0003631561,0.0002536648],"domain_scores_gemma":[0.9987918,0.00004313474,0.000125336,0.0003356654,0.0006600701,0.00004397293],"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":[0.0001509031,0.0001142937,0.0002527446,0.0002061406,0.0007826336,3.738961e-7,0.00009602668,0.01083016,0.882092,0.08634305,0.002414281,0.01671737],"study_design_scores_gemma":[0.00191083,0.0003763143,0.0008890337,0.00002433495,0.001844348,0.000004299617,0.0004066227,0.1340914,0.6082109,0.01282261,0.2388663,0.0005529732],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7107778,0.03557345,0.2494342,0.0001875808,0.001254976,0.0008502166,0.0002971489,0.00004470284,0.001579899],"genre_scores_gemma":[0.9863086,0.0010169,0.01155835,0.00009696138,0.0003065061,0.0001633294,0.0003822345,0.00004472102,0.0001224234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2755308,"threshold_uncertainty_score":0.8085207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03146197752913431,"score_gpt":0.3105886317291696,"score_spread":0.2791266542000353,"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."}}