{"id":"W3159570643","doi":"10.3390/catal11050576","title":"Effect of Background Water Matrices on Pharmaceutical and Personal Care Product Removal by UV-LED/TiO2","year":2021,"lang":"en","type":"article","venue":"Catalysts","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Canadian Natural Resources Limited","keywords":"Ultrapure water; Effluent; Wastewater; Environmental impact of pharmaceuticals and personal care products; Chemistry; Portable water purification; Surface water; Partition coefficient; Reaction rate constant; Photocatalysis; Environmental chemistry; Water treatment; Pulp and paper industry; Environmental science; Environmental engineering; Chromatography; Kinetics; Organic chemistry; Catalysis","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":[0.0004309244,0.0006078132,0.0003702717,0.0001838789,0.0002684311,0.0007992012,0.0003083995,0.0003824829,0.000764134],"category_scores_gemma":[0.0006344558,0.0002018715,0.0002884117,0.0001668873,0.0002430567,0.000367326,0.000361926,0.0003960234,0.0002888024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003907031,"about_ca_system_score_gemma":0.0002818881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002636096,"about_ca_topic_score_gemma":0.004726101,"domain_scores_codex":[0.9993829,0.0001363188,0.00003757548,0.0001362957,0.0002347789,0.00007222129],"domain_scores_gemma":[0.9997405,0.0000906323,0.00004593499,0.00001907916,0.00008428964,0.00001959046],"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":[0.00006826,0.00002303644,0.0003105658,0.00007027732,0.00001457995,0.00003970665,0.00002909747,0.00008635228,0.9972624,0.00001418177,0.00001939112,0.002062246],"study_design_scores_gemma":[0.000003550091,0.0001794592,0.001331539,0.000004494476,0.00001624181,0.00004670186,0.00004350465,0.0004695557,0.9971973,0.00001008982,0.0006923326,0.000005256541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992066,0.001764781,0.00475958,0.00005835269,0.0000322891,0.00005392945,0.0001228627,0.00007184305,0.001070478],"genre_scores_gemma":[0.9890219,0.001226342,0.007808637,0.00008357838,0.00001136892,0.00004460532,0.0001638006,0.00004951278,0.001590203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002636096,"threshold_uncertainty_score":0.005241454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378245501206576,"score_gpt":0.2945864606122218,"score_spread":0.2808040056001561,"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."}}