{"id":"W3041671621","doi":"10.1016/j.chroma.2020.461399","title":"Recent advances in analytical methods for the determination of citrinin in food matrices","year":2020,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"CanAm Bioresearch (Canada)","funders":"","keywords":"Citrinin; Monascus; Quechers; Chemistry; Chromatography; Sample preparation; Extraction (chemistry); Mycotoxin; Metabolite; Secondary metabolite; Food science; Pesticide; Pesticide residue; Biology; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003315764,0.001246599,0.001084358,0.001687984,0.0003757451,0.001346771,0.001303963,0.001091355,0.001361263],"category_scores_gemma":[0.002963827,0.0006482463,0.000585073,0.001177018,0.0009257764,0.001388641,0.0009612509,0.002123127,0.001079741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007598458,"about_ca_system_score_gemma":0.0009502147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208476,"about_ca_topic_score_gemma":0.00226876,"domain_scores_codex":[0.9981326,0.0003932637,0.0001079013,0.0005055319,0.0007627092,0.000097936],"domain_scores_gemma":[0.9959331,0.001968998,0.0004710198,0.0002558374,0.001211076,0.0001600318],"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":[0.0002989475,0.0001712953,0.002602097,0.005777084,0.0002708221,0.0001669675,0.000167466,0.0009312708,0.5857261,0.002997153,0.002903525,0.3979873],"study_design_scores_gemma":[0.00004804291,0.00069273,0.0101716,0.0007282742,0.0004412495,0.003775761,0.000292169,0.01217155,0.6905083,0.005450577,0.2754611,0.0002587321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.03425366,0.7346656,0.2185405,0.002993204,0.001277093,0.0001491009,0.0004190168,0.0005808299,0.00712103],"genre_scores_gemma":[0.1455016,0.5414755,0.294821,0.003313673,0.002579752,0.0002739988,0.000909413,0.000183155,0.01094197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003315764,"threshold_uncertainty_score":0.01753563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384489994713771,"score_gpt":0.3144440670243976,"score_spread":0.28059916707726,"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."}}