{"id":"W2907649473","doi":"10.1016/j.aca.2018.12.044","title":"Development and validation of a fully automated solid phase microextraction high throughput method for quantitative analysis of multiresidue veterinary drugs in chicken tissue","year":2018,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chemistry; Chromatography; Solid-phase microextraction; Veterinary drug; Desorption; Veterinary Drugs; Extraction (chemistry); Mass spectrometry; Detection limit; Solid phase extraction; Gas chromatography–mass spectrometry; Adsorption; Veterinary medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008174762,0.0003080437,0.000929198,0.0004015152,0.00008123195,0.00002325642,0.0002452447,0.000218535,0.0002651984],"category_scores_gemma":[0.0005749977,0.0003087937,0.000142074,0.0009256486,0.0002233903,0.0001495449,0.00012307,0.0001580615,0.000002380631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821969,"about_ca_system_score_gemma":0.0001816294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009322409,"about_ca_topic_score_gemma":0.00003873432,"domain_scores_codex":[0.9974951,0.0000940891,0.001137319,0.0006194906,0.0003100862,0.0003438966],"domain_scores_gemma":[0.9978241,0.0008013607,0.0005355661,0.0003878566,0.0003343325,0.0001167995],"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":[0.0005993745,0.0004744417,0.0002094298,0.0003428368,0.001830867,0.000002093717,0.002735135,0.000009320709,0.9916172,0.0001429112,0.00003317159,0.002003226],"study_design_scores_gemma":[0.00131159,0.0001404484,0.001613823,0.0001010952,0.001079203,0.000004005997,0.0005855404,0.1148656,0.8795523,0.0001098418,0.0003510389,0.0002854943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795996,0.000009048025,0.01933008,0.0001565825,0.00002297499,0.0001925922,0.0001007116,0.00006788674,0.0005205059],"genre_scores_gemma":[0.6306618,0.00001211895,0.3689497,0.00001333514,0.00001919086,0.00002321925,0.000214212,0.0000199837,0.00008641769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3496196,"threshold_uncertainty_score":0.9999364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636900856208476,"score_gpt":0.4137543262335678,"score_spread":0.367385317671483,"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."}}