{"id":"W3108281763","doi":"10.38212/2224-6614.1267","title":"Nanomaterial-based fluorescent biosensor for veterinary drug detection in foods","year":2020,"lang":"en","type":"review","venue":"Journal of Food and Drug Analysis","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Veterinary drug; Aptamer; Veterinary Drugs; Nanomaterials; Nanotechnology; Bioconjugation; Food safety; Biosensor; Drug detection; Complex matrix; Molecularly imprinted polymer; Fluorescence; Drug; Biochemical engineering; Chemistry; Biotechnology; Materials science; Veterinary medicine; Pharmacology; Medicine; Biology; Food science; Molecular biology; Chromatography; Biochemistry; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004554011,0.0003586246,0.001643353,0.0006616563,0.00006517556,0.00005415065,0.0001834654,0.0002315259,5.97832e-7],"category_scores_gemma":[0.00008613157,0.000266278,0.001524009,0.0006415905,0.00005639452,0.000006202063,0.00005691037,0.0001734156,1.978194e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005085169,"about_ca_system_score_gemma":0.0001173815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005073779,"about_ca_topic_score_gemma":0.00005830931,"domain_scores_codex":[0.9980763,0.0002302878,0.0009357572,0.0003911024,0.00016004,0.000206492],"domain_scores_gemma":[0.9985035,0.00004587213,0.0009512376,0.0002349348,0.00014434,0.0001201169],"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":[0.0004346297,0.0002102921,0.00001391618,0.003635563,0.005681229,0.00005378923,0.00003147583,0.00002453075,0.05840746,0.000002293229,0.000229395,0.9312754],"study_design_scores_gemma":[0.0006980069,0.001803518,0.000006092228,0.001408354,0.01062695,0.000091181,0.00005248338,0.000157576,0.1302165,0.00002042608,0.8543021,0.0006167825],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01525314,0.9822283,0.001916248,0.00008405547,0.0001170776,0.0002715034,0.0001129113,0.0000123875,0.000004386464],"genre_scores_gemma":[0.2315353,0.7656514,0.002305052,0.00003693411,0.0003266054,0.00001009619,0.00008784175,0.00002921351,0.00001749085],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9306586,"threshold_uncertainty_score":0.999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927557748274663,"score_gpt":0.3108193198810149,"score_spread":0.2915437423982682,"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."}}