{"id":"W3120042105","doi":"10.1016/j.cscee.2021.100079","title":"Microplastics as vectors of pharmaceuticals in aquatic organisms – An overview of their environmental implications","year":2021,"lang":"en","type":"article","venue":"Case Studies in Chemical and Environmental Engineering","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Generalitat de Catalunya; Ministerio de Ciencia, Innovación y Universidades; Centres de Recerca de Catalunya; Canadian Institute for Advanced Research","keywords":"Microplastics; Bioaccumulation; Biomagnification; Aquatic environment; Aquatic ecosystem; Environmental chemistry; Bioavailability; Toxicity; Environmental science; Biology; Chemistry; Ecology; Pharmacology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008290611,0.0001608096,0.0002628857,0.00003201013,0.00002386978,0.000002969787,0.00006281686,0.00005950153,0.0002119257],"category_scores_gemma":[0.00005639633,0.0001551388,0.00003304525,0.0001205859,0.0002900226,0.0000612322,0.0002562681,0.0001278044,0.00000629417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001871096,"about_ca_system_score_gemma":0.00000456583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001941854,"about_ca_topic_score_gemma":0.000009384852,"domain_scores_codex":[0.9990869,0.00002012491,0.0003428173,0.0002529532,0.0000974467,0.0001997714],"domain_scores_gemma":[0.9995334,0.0001938137,0.00005194293,0.0001364209,7.436836e-7,0.00008370796],"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":[0.000007855439,0.0002155122,0.01416961,0.00006617352,0.0000216654,0.000128987,0.0006137024,0.002030988,0.9812126,0.00003823944,0.000004394044,0.001490304],"study_design_scores_gemma":[0.0009666035,0.00009357429,0.01659601,0.0001403934,0.00006219701,0.00153938,0.002138984,0.01394002,0.9635016,0.000337104,0.0002808674,0.0004032684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970391,0.002578459,0.00009957787,0.00001210582,0.00005314482,0.00008243025,0.0001006039,0.000004721559,0.000029888],"genre_scores_gemma":[0.9965329,0.002591488,0.0007969656,0.00001911428,0.00001046414,0.000008277148,0.00002238497,0.00001316515,0.000005195309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01771097,"threshold_uncertainty_score":0.6326378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03632948094833965,"score_gpt":0.2802720956126962,"score_spread":0.2439426146643566,"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."}}