{"id":"W3080128336","doi":"10.1016/j.foodchem.2020.127847","title":"Mass spectrometry-based untargeted metabolomics approach for differentiation of beef of different geographic origins","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Shenzhen Graduate School, Peking University; Science, Technology and Innovation Commission of Shenzhen Municipality; Hong Kong Polytechnic University","keywords":"Metabolomics; Beef cattle; Food science; Geographical indication; Biotechnology; Metabolite; Metabolome; Business; Biology; Chemistry; Geography; Animal science; Biochemistry; Chromatography","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":[],"consensus_categories":[],"category_scores_codex":[0.00008832994,0.0002620331,0.0005405818,0.00005291347,0.00004498687,0.00000900026,0.0002811652,0.0001794105,0.00002609753],"category_scores_gemma":[0.0001324605,0.0002408717,0.000357807,0.0002397624,0.00009759345,0.000002695273,0.00007614825,0.0001045706,1.516526e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001475042,"about_ca_system_score_gemma":0.00004735941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002715718,"about_ca_topic_score_gemma":8.514599e-7,"domain_scores_codex":[0.9986405,0.00002262731,0.0004268447,0.0004476442,0.0001919942,0.0002703487],"domain_scores_gemma":[0.9990461,0.0000235993,0.0003453871,0.0003305025,0.000148778,0.0001056056],"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":[0.0002248518,0.0002002811,0.003877379,0.0005441681,0.0004951297,8.061125e-8,0.0000141916,0.00006116015,0.9940694,0.000383648,0.00009446841,0.00003524367],"study_design_scores_gemma":[0.001344347,0.0005437251,0.0009793636,0.000006025937,0.0001837275,4.116769e-7,0.00006493412,0.0009393698,0.9948333,0.000172364,0.0007067255,0.0002256917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116884,0.00117288,0.08583635,0.0001003671,0.00005423817,0.0002778327,0.0005076742,0.00001500239,0.0003472968],"genre_scores_gemma":[0.9811,0.0001993014,0.01735095,0.00005263216,0.0001847941,0.00005080902,0.001007708,0.00003187523,0.00002198795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06941158,"threshold_uncertainty_score":0.9822462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301581107823522,"score_gpt":0.2177744911345303,"score_spread":0.2047586800562951,"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."}}