{"id":"W2640707900","doi":"10.1021/acs.analchem.7b00925","title":"Development of a Liquid Chromatography–High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Vetenskapsrådet; Karolinska Institutet; Hjärt-Lungfonden; Novo Nordisk Fonden; AstraZeneca","keywords":"Chemistry; Metabolomics; Chromatography; Metabolite; Fragmentation (computing); Mass spectrometry; High resolution; Liquid chromatography–mass spectrometry; Resolution (logic); Biochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002087054,0.001432101,0.001134533,0.00226283,0.0008194825,0.001093113,0.001167074,0.001534184,0.001447026],"category_scores_gemma":[0.002426657,0.0007318349,0.001056656,0.001108568,0.000518076,0.001342133,0.001344232,0.001548331,0.001838254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004555722,"about_ca_system_score_gemma":0.001363745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008123039,"about_ca_topic_score_gemma":0.001376105,"domain_scores_codex":[0.9976865,0.0003280485,0.0002128229,0.000749693,0.0008595629,0.0001634505],"domain_scores_gemma":[0.9989191,0.0002624878,0.0001588146,0.0001349954,0.0004485648,0.00007592767],"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":[0.00007826293,0.00008024766,0.0007985187,0.0001982595,0.0000516542,0.0001670347,0.00003239251,0.0002741034,0.96737,0.0003867177,0.0003514251,0.03021127],"study_design_scores_gemma":[0.00004920651,0.0004428818,0.003193724,0.00003453619,0.0001162386,0.001688961,0.00003093758,0.0160523,0.9664205,0.0005538674,0.01129943,0.0001174356],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05797456,0.002212521,0.9326113,0.0003089755,0.0001497319,0.001121576,0.00106844,0.00267997,0.001872982],"genre_scores_gemma":[0.06938028,0.001195683,0.9247684,0.0005735552,0.00007092798,0.0008860148,0.001530075,0.0002191891,0.001375933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00226283,"threshold_uncertainty_score":0.01103753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419515424885228,"score_gpt":0.3070907712126256,"score_spread":0.2828956169637734,"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."}}