{"id":"W3111261814","doi":"10.1016/j.envpol.2020.116313","title":"Combining an effect-based methodology with chemical analysis for antibiotics determination in wastewater and receiving freshwater and marine environment","year":2020,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Canadian Institute for Advanced Research","keywords":"Seawater; Wastewater; Sewage treatment; Effluent; Environmental science; Environmental chemistry; Antibiotics; Mediterranean sea; Ciprofloxacin; Sewage; Ecotoxicity; Nonylphenol; Mediterranean climate; Biology; Environmental engineering; Ecology; Chemistry; Microbiology; Toxicity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001686368,0.001334389,0.0011961,0.001858416,0.0007513568,0.0009496514,0.001433237,0.002359855,0.001028541],"category_scores_gemma":[0.001705262,0.000784866,0.001772759,0.0009227053,0.001157571,0.001025034,0.001503274,0.001587406,0.0008547629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006102517,"about_ca_system_score_gemma":0.001771084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002739224,"about_ca_topic_score_gemma":0.006842903,"domain_scores_codex":[0.996971,0.0006246615,0.0001375688,0.0008545401,0.001225187,0.00018689],"domain_scores_gemma":[0.9989086,0.0003820218,0.0001240619,0.0001123027,0.0004038953,0.00006909807],"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":[0.0001670668,0.00011747,0.001310053,0.000185679,0.0000903324,0.00003858481,0.00004134189,0.0003543358,0.9867743,0.0001682306,0.00008243706,0.01067017],"study_design_scores_gemma":[0.00003502335,0.0006474513,0.003994687,0.00001789163,0.0001830593,0.0002226174,0.00005162052,0.005673943,0.9867235,0.0001789232,0.002213211,0.00005805368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4184403,0.005362818,0.5641677,0.000700082,0.0008718957,0.001172322,0.001139028,0.001623322,0.00652265],"genre_scores_gemma":[0.6145722,0.003648654,0.3712351,0.001108324,0.0001795775,0.0008103123,0.0005637627,0.0001496522,0.007732496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002739224,"threshold_uncertainty_score":0.008918464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03138036204307578,"score_gpt":0.2707179611683856,"score_spread":0.2393375991253099,"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."}}