{"id":"W2788310517","doi":"10.1007/s11306-018-1321-4","title":"Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine","year":2018,"lang":"en","type":"review","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"King Abdullah University of Science and Technology; Biogen","keywords":"Normalization (sociology); Metabolomics; Proton NMR; Data processing; NMR spectra database; Computer science; Biological system; Data mining; Chemistry; Spectral line; Nuclear magnetic resonance; Biology; Physics; Chromatography; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005812995,0.0005255399,0.001783873,0.0001757777,0.0001043883,0.00006395625,0.000549413,0.0002790273,0.000004584863],"category_scores_gemma":[0.0001684974,0.0003761309,0.0002020344,0.0002361513,0.0002886888,0.00002046772,0.0003835648,0.0001599986,5.023953e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001262111,"about_ca_system_score_gemma":0.0005180798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001552077,"about_ca_topic_score_gemma":0.00003818532,"domain_scores_codex":[0.9977461,0.00007075512,0.0007526522,0.0008432214,0.0001685237,0.0004187352],"domain_scores_gemma":[0.9978999,0.00003828506,0.0008113828,0.0008703331,0.0003103351,0.00006975709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001025303,0.0006151672,0.00005296059,0.0355768,0.002775146,0.000005356094,0.0001342953,0.000003349953,0.01983596,0.003910599,0.001133282,0.9349318],"study_design_scores_gemma":[0.001391718,0.00209645,0.0000271406,0.002237482,0.00351614,0.0000513612,0.0003235464,0.0001132136,0.01313323,0.0006862389,0.9755052,0.0009182903],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.006470803,0.9898093,0.001893355,0.00006698073,0.00008271675,0.0007188651,0.0008076698,0.00001070015,0.0001395479],"genre_scores_gemma":[0.005853165,0.9766468,0.01613718,0.00004013261,0.000348151,0.00006350563,0.0007801991,0.00008642243,0.00004443642],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9743719,"threshold_uncertainty_score":0.999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05696740823780422,"score_gpt":0.330283323267998,"score_spread":0.2733159150301938,"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."}}