{"id":"W2331835098","doi":"10.1021/ac3004659","title":"Depth-Profiling of Environmental Pharmaceuticals in Biological Tissue by Solid-Phase Microextraction","year":2012,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Environment and Climate Change Canada; University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Solid-phase microextraction; Chemistry; Chromatography; In vivo; Contamination; Profiling (computer programming); Sampling (signal processing); Biological system; Process engineering; Environmental chemistry; Gas chromatography–mass spectrometry; Computer science; Biotechnology; Mass spectrometry; Ecology","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":[0.0001934943,0.0004446473,0.0003136299,0.0003296325,0.0001719679,0.0003146098,0.0002474008,0.0003645687,0.0003965179],"category_scores_gemma":[0.000313487,0.0002088476,0.0002095635,0.0002476287,0.0002527536,0.0003592015,0.0003427676,0.0004353929,0.0002557805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002619277,"about_ca_system_score_gemma":0.0004325556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472506,"about_ca_topic_score_gemma":0.004982721,"domain_scores_codex":[0.9998102,0.00001804194,0.00001053257,0.00005499172,0.00008609601,0.00002025925],"domain_scores_gemma":[0.999835,0.00006223166,0.00003547582,0.00001315032,0.00004330393,0.00001081968],"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":[0.00001445914,0.00000373695,0.0002042906,0.00001939517,0.000001820219,0.000007243215,0.000005561097,0.0001118989,0.9977359,0.00002229759,0.000005762186,0.001867639],"study_design_scores_gemma":[0.000003447933,0.0000918065,0.002128263,0.000003074298,0.00000827742,0.00003712751,0.00001747447,0.001901898,0.9952187,0.00005496774,0.0005298478,0.000005117065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8429997,0.001774739,0.1510613,0.0001502292,0.00002925744,0.0002450784,0.001046855,0.000257935,0.00243491],"genre_scores_gemma":[0.820597,0.003178916,0.171219,0.0001239617,0.00001572895,0.0002002324,0.0006659941,0.00004113443,0.003958073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001472506,"threshold_uncertainty_score":0.002927899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04280746940977179,"score_gpt":0.3828795205014058,"score_spread":0.340072051091634,"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."}}