{"id":"W7117679753","doi":"10.1016/j.microc.2025.116764","title":"Simultaneous determination of multiclass veterinary drug residues in milk and dairy products using an optimized LLE-SPE-UPLC-MS/MS method: Application to penicillins, quinolones, tetracyclines, and antiparasitics","year":2025,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"Earmarked Fund for China Agriculture Research System; Jiangsu Association for Science and Technology; National Natural Science Foundation of China; Jiangsu Agricultural Science and Technology Innovation Fund","keywords":"Veterinary drug; Veterinary Drugs; Residue (chemistry); Extraction (chemistry); Matrix (chemical analysis); Maximum Residue Limit; Gradient elution; Detection limit; Solid phase extraction","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":[0.0005105792,0.0001560614,0.0003487755,0.00006954288,0.0001406315,0.00006119419,0.0001785938,0.0001407181,0.000005528555],"category_scores_gemma":[0.0003224686,0.00008183891,0.00004633924,0.0003658425,0.00007343037,0.0001245183,0.00009458196,0.0002583778,3.947671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004693061,"about_ca_system_score_gemma":0.00002219896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000309916,"about_ca_topic_score_gemma":0.0001585142,"domain_scores_codex":[0.9985944,0.0001736068,0.0005164931,0.0003447731,0.0001475033,0.0002232134],"domain_scores_gemma":[0.9989997,0.000457841,0.000168929,0.00007532262,0.0001845588,0.0001136898],"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":[0.0001909317,0.0001255343,0.0046191,0.0000282512,0.00001080425,0.0000168438,0.0001185526,0.000386017,0.95081,0.000002928507,0.00001034845,0.04368071],"study_design_scores_gemma":[0.001558786,0.0003564617,0.09832178,0.000428707,0.000218227,0.0005578197,0.0006881446,0.1316497,0.7635605,0.0007635884,0.001270855,0.0006254073],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931812,0.0007972163,0.004948835,0.0008346127,0.00002395151,0.000184205,0.00001283426,0.00001061644,0.000006526812],"genre_scores_gemma":[0.9361886,0.0004584322,0.06310268,0.0001088726,0.00009817183,0.000002942011,0.00002381714,0.000001727085,0.00001469877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1872494,"threshold_uncertainty_score":0.3337294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087628906890116,"score_gpt":0.3069291121282798,"score_spread":0.2860528230593787,"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."}}