{"id":"W4392738984","doi":"10.1007/s11306-024-02090-6","title":"Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging","year":2024,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; The Metabolomics Innovation Centre","funders":"","keywords":"Metabolomics; Reuse; Computational biology; Perspective (graphical); Computer science; Data science; Biology; Bioinformatics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008158842,0.0003753609,0.0006058638,0.0002282751,0.0001372456,0.00119554,0.0004713673,0.0002405128,0.000008101139],"category_scores_gemma":[0.0006234196,0.000341457,0.0001147401,0.0002197919,0.0003184503,0.0004029323,0.001030313,0.0002670854,0.000001718859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002361702,"about_ca_system_score_gemma":0.00009575497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003013494,"about_ca_topic_score_gemma":0.00005663868,"domain_scores_codex":[0.9978459,0.0001205937,0.0004125186,0.001034745,0.0001846092,0.0004016305],"domain_scores_gemma":[0.998033,0.0001433923,0.0001296969,0.001391987,0.0001678429,0.0001340244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001034019,0.0005548926,0.003602847,0.0009514523,0.004964732,0.0000869537,0.0031253,0.0005212539,0.8041177,0.06984533,0.00356446,0.1076311],"study_design_scores_gemma":[0.005902577,0.001029654,0.01118491,0.002099696,0.003536264,0.0002225947,0.0431363,0.03352218,0.2106522,0.007221797,0.6777291,0.003762724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5968547,0.3985202,0.002482276,0.001274449,0.0005433417,0.0002077727,0.0000688801,0.00002700151,0.00002133092],"genre_scores_gemma":[0.5101714,0.4798304,0.009224159,0.0002606316,0.0002401758,0.00001264396,0.0001222808,0.00005552886,0.00008287357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6741646,"threshold_uncertainty_score":0.9999037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03363435774878694,"score_gpt":0.2885542869952447,"score_spread":0.2549199292464577,"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."}}