{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005960086,0.00007818877,0.0002506362,0.00004785546,0.00002634236,0.00001696954,0.000256786,0.00005822045,0.00002722966],"category_scores_gemma":[0.000183038,0.00002303543,0.0001941448,0.001174433,0.00003309804,0.0001581921,0.00001902698,0.0001302873,1.401199e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007579072,"about_ca_system_score_gemma":0.00000691388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001573664,"about_ca_topic_score_gemma":0.00006643753,"domain_scores_codex":[0.9990352,0.0001091075,0.0004896547,0.00009176817,0.0001536659,0.0001206315],"domain_scores_gemma":[0.9987681,0.0007238982,0.0003399479,0.0000217675,0.00009725918,0.00004902871],"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":[0.0001978875,0.0001588977,0.01198245,0.00003901381,0.00002752736,0.000003382782,0.0003309108,0.00002200308,0.02404267,0.0005527899,0.0001380993,0.9625044],"study_design_scores_gemma":[0.00251919,0.009031763,0.4279568,0.0003957457,0.0001659832,0.00009610249,0.003847606,0.004609351,0.03668236,0.007866369,0.5063041,0.000524619],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539016,0.03941004,0.001364009,0.004447353,0.0001344198,0.0003757552,0.00001495266,0.000007510852,0.0003442891],"genre_scores_gemma":[0.9831071,0.0101535,0.006429643,0.0001349881,0.0001635705,0.000008511929,0.000001422792,6.204105e-7,6.562321e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9619797,"threshold_uncertainty_score":0.09393577,"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."}}