{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005912948,0.0009418341,0.0006315425,0.000730823,0.000516625,0.0004668977,0.0004689251,0.001021766,0.0007913188],"category_scores_gemma":[0.0006750948,0.0004301163,0.0004555942,0.0003939765,0.0004213975,0.0004417169,0.0006109772,0.0007189233,0.0004523534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005093652,"about_ca_system_score_gemma":0.001092764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001676149,"about_ca_topic_score_gemma":0.005893253,"domain_scores_codex":[0.999288,0.00008131067,0.00004680125,0.0002243874,0.0002994125,0.00006009772],"domain_scores_gemma":[0.9997212,0.000065641,0.00006313236,0.00001960814,0.00009441566,0.00003593618],"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":[0.0001726971,0.00005511493,0.0009738598,0.0001345057,0.00005086638,0.00007929106,0.0000396367,0.0001639543,0.985723,0.00006225158,0.0001395176,0.01240527],"study_design_scores_gemma":[0.00002883873,0.0003537014,0.008586953,0.00002206716,0.00007768352,0.0009689109,0.00005187606,0.005690642,0.9799706,0.00009294973,0.004109693,0.00004607436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8718533,0.01127736,0.1083868,0.0005826778,0.0002495216,0.0004094575,0.002053158,0.001576913,0.003610861],"genre_scores_gemma":[0.8574978,0.003678118,0.1289343,0.0008609716,0.00006818973,0.0003485341,0.001016409,0.0001321272,0.007463665],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001676149,"threshold_uncertainty_score":0.003695726,"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."}}