{"id":"W2318392309","doi":"10.1007/s00216-016-9493-0","title":"A modified QuEChERS approach for the screening of dioxins and furans in sediments","year":2016,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of the Environment, Conservation and Parks","funders":"Ministry of Environment; Agilent Technologies","keywords":"Quechers; Extraction (chemistry); Chromatography; Detection limit; Sample preparation; Chemistry; Environmental science; Pesticide residue; Pesticide","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":[0.0003028898,0.0001304836,0.0002067291,0.00001274296,0.00004184931,0.00001350617,0.0001523065,0.0001041156,0.0002077432],"category_scores_gemma":[0.0002461869,0.000066011,0.00005984678,0.0001744614,0.0007492258,0.00006440358,0.0001188355,0.00009347037,0.000001476707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004449662,"about_ca_system_score_gemma":0.000009491741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004887772,"about_ca_topic_score_gemma":0.00000506995,"domain_scores_codex":[0.9989622,0.00001349519,0.0002345165,0.0002979384,0.000200614,0.0002912505],"domain_scores_gemma":[0.9992772,0.0003306545,0.00003983043,0.0001755263,0.000002194642,0.000174645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008293927,0.0008512823,0.1409625,0.0001956898,0.0006407464,0.00001983367,0.0003712431,0.00009468594,0.4816712,0.001398557,0.0005148833,0.37245],"study_design_scores_gemma":[0.00788924,0.000289525,0.389883,0.0001569947,0.0008286641,0.00008807865,0.0009868738,0.4481254,0.1443299,0.005094632,0.001054019,0.001273627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9386117,0.0001841533,0.04541403,0.006045032,0.00001418462,0.0003676708,0.0001340575,0.00002959387,0.009199559],"genre_scores_gemma":[0.9984052,0.00003562676,0.0007683316,0.00009113388,0.0000214807,0.000003556403,4.658514e-7,0.000007917293,0.0006662484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4480307,"threshold_uncertainty_score":0.2760554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128404725685696,"score_gpt":0.2462169221764942,"score_spread":0.2249328749196372,"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."}}