{"id":"W2035079397","doi":"10.1021/es903064t","title":"Tissue-Specific In Vivo Bioconcentration of Pharmaceuticals in Rainbow Trout (<i>Oncorhynchus mykiss</i>) Using Space-Resolved Solid-Phase Microextraction","year":2010,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Solid-phase microextraction; Rainbow trout; Chromatography; Bioconcentration; Chemistry; In vivo; Analyte; Extraction (chemistry); Environmental chemistry; Mass spectrometry; Gas chromatography–mass spectrometry; Fish <Actinopterygii>; Biology; Bioaccumulation; Fishery","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","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007209065,0.0003260676,0.0003679834,0.0004934687,0.0001839177,0.00003394505,0.0006733071,0.0002768753,0.003020513],"category_scores_gemma":[0.00005220828,0.0003231998,0.00005882144,0.00150417,0.005002348,0.0008104962,0.0004573755,0.000723221,0.0001692897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008324264,"about_ca_system_score_gemma":0.00002978103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001238656,"about_ca_topic_score_gemma":0.0000962354,"domain_scores_codex":[0.9968254,0.00005754748,0.0006538594,0.0008610793,0.0006078635,0.0009942305],"domain_scores_gemma":[0.9990008,0.00004684666,0.0002262395,0.0004422406,0.000002223334,0.0002816077],"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":[0.00005145674,0.001025181,0.05445012,0.000003961305,0.000002351728,0.00004057507,0.0001472208,0.0004536789,0.9241762,0.00007902623,0.00001523935,0.01955506],"study_design_scores_gemma":[0.001747506,0.0001682965,0.02006052,0.00002254573,0.00001008464,0.000069516,0.0002660605,0.003543966,0.9672875,0.0003349439,0.006163135,0.0003259539],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969353,0.00009187809,0.0005961218,0.0005363896,0.0001926501,0.0006373259,0.00002404065,0.00004613972,0.0009401243],"genre_scores_gemma":[0.9969858,0.0001972951,0.002566143,0.0001205327,0.00002909751,0.00001117557,0.000003859344,0.00002220383,0.00006392751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04311134,"threshold_uncertainty_score":0.999922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115587229377118,"score_gpt":0.3437867944976762,"score_spread":0.322630922203905,"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."}}