{"id":"W4385968619","doi":"10.1021/acs.analchem.3c02888","title":"Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; State Administration of Traditional Chinese Medicine of the People's Republic of China; National Natural Science Foundation of China","keywords":"Metabolomics; Chemistry; Data mining; Data acquisition; Identification (biology); Data quality; Computational biology; Computer science; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004910214,0.002128735,0.001228537,0.003277109,0.001011854,0.002229935,0.001316078,0.001340633,0.003050399],"category_scores_gemma":[0.006294592,0.0009191284,0.001608516,0.001794363,0.0009730814,0.00325961,0.003463608,0.002152228,0.002034905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006067615,"about_ca_system_score_gemma":0.001683281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008601951,"about_ca_topic_score_gemma":0.00158148,"domain_scores_codex":[0.9973694,0.000346787,0.0002429621,0.0008565088,0.0009583107,0.0002261045],"domain_scores_gemma":[0.9960443,0.001036316,0.000544458,0.0006430085,0.001527115,0.0002049242],"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":[0.001306924,0.000233532,0.008338648,0.0009294944,0.0004628885,0.0007098213,0.0003037863,0.001207243,0.8969262,0.001239674,0.003282204,0.08505964],"study_design_scores_gemma":[0.0001602425,0.0005842234,0.01783743,0.0001072629,0.000381375,0.002098147,0.0001985426,0.03812518,0.9207711,0.002953903,0.01650858,0.0002740742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3303025,0.005065727,0.640967,0.001429375,0.0005786206,0.0008608931,0.005983401,0.009989097,0.004823336],"genre_scores_gemma":[0.3502268,0.0032603,0.6288404,0.002244442,0.0002498038,0.00110211,0.008058165,0.002338809,0.003679236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004910214,"threshold_uncertainty_score":0.02596802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05455253966224419,"score_gpt":0.3381075818757255,"score_spread":0.2835550422134814,"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."}}