{"id":"W4303470625","doi":"10.1002/9783527833092.ch4","title":"Data Processing in Metabolomics Capillary Electrophoresis–Mass Spectrometry","year":2022,"lang":"en","type":"other","venue":"","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Metabolomics; Normalization (sociology); Data pre-processing; Preprocessor; Data acquisition; Data processing; Mass spectrometry; Database normalization; Data extraction; Computer science; Metabolome; Proteomics; Data mining; Chromatography; Chemistry; Pattern recognition (psychology); Database; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003364409,0.001865559,0.001228637,0.003616343,0.001158143,0.002926636,0.001497729,0.0008763741,0.02994452],"category_scores_gemma":[0.007743229,0.00073095,0.0008515092,0.005430079,0.0007471994,0.001750007,0.001674781,0.001541628,0.02702234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006592005,"about_ca_system_score_gemma":0.001409975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001095798,"about_ca_topic_score_gemma":0.001204283,"domain_scores_codex":[0.9967659,0.0007815273,0.0003568648,0.0008668805,0.001106857,0.000122048],"domain_scores_gemma":[0.9967273,0.00143231,0.0002457479,0.0006139993,0.000892499,0.00008819724],"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":[0.0005603457,0.0001966164,0.002574306,0.005411256,0.0002216535,0.0007217688,0.0008010503,0.002935624,0.07930645,0.02246479,0.1583252,0.7264811],"study_design_scores_gemma":[0.00006958141,0.0001782395,0.006441044,0.0006427685,0.0001287417,0.001349153,0.0003234223,0.01552904,0.1434908,0.02288871,0.8087274,0.0002310688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005391626,0.004201161,0.8973153,0.0007220461,0.000640981,0.00195263,0.02565698,0.03680129,0.02731807],"genre_scores_gemma":[0.0171226,0.004480785,0.9314913,0.0008616671,0.0003435643,0.002976283,0.02130332,0.006945889,0.0144745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02994452,"threshold_uncertainty_score":0.1001744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673390150570173,"score_gpt":0.2635972271180446,"score_spread":0.2468633256123429,"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."}}