{"id":"W3088356096","doi":"10.1016/j.foodchem.2020.128127","title":"Overcoming matrix effects in the analysis of pyrethroids in honey by a fully automated direct immersion solid-phase microextraction method using a matrix-compatible fiber","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Insect and Pesticide Research","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Clinical Trial Center, China Medical University Hospital","keywords":"Repeatability; Matrix (chemical analysis); Solid-phase microextraction; Chromatography; Reproducibility; Chemistry; Gas chromatography–mass spectrometry; Mass spectrometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004123391,0.0005809125,0.0002845212,0.0001685103,0.0002958231,0.0004031732,0.0004421565,0.000532299,0.0006081231],"category_scores_gemma":[0.0004681279,0.0003419578,0.0002953314,0.0001230037,0.0002898365,0.0004297671,0.0004036535,0.0005696507,0.0003491432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002194933,"about_ca_system_score_gemma":0.0005164138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413976,"about_ca_topic_score_gemma":0.004712423,"domain_scores_codex":[0.9994556,0.00006217964,0.00002266882,0.0001667475,0.0002472964,0.00004547082],"domain_scores_gemma":[0.9997552,0.00009228533,0.00003893586,0.00002533496,0.00007523508,0.00001297978],"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":[0.00002672982,0.00001061908,0.0001344191,0.00002706225,0.000005884135,0.00001392843,0.00001834392,0.0000816789,0.9952205,0.00004474158,0.00001826947,0.00439772],"study_design_scores_gemma":[0.000005369838,0.0001348224,0.001235351,0.000003382651,0.00001356174,0.0001367254,0.00001519386,0.002718156,0.9944721,0.00004138892,0.001215037,0.000008909369],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7447363,0.001243075,0.2512627,0.0001645988,0.00009414543,0.0002470378,0.0002084592,0.0006013571,0.001442428],"genre_scores_gemma":[0.705651,0.001160343,0.2884109,0.0001158473,0.00003134363,0.0001797826,0.0002856663,0.0001177177,0.004047477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001413976,"threshold_uncertainty_score":0.002811551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924534732456809,"score_gpt":0.3648492940568605,"score_spread":0.3356039467322924,"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."}}