{"id":"W3096527464","doi":"10.1016/j.watres.2020.116602","title":"Diphenylamine Antioxidants in wastewater influent, effluent, biosolids and landfill leachate: Contribution to environmental releases","year":2020,"lang":"en","type":"article","venue":"Water Research","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Harbin Institute of Technology","keywords":"Effluent; Biosolids; Leachate; Wastewater; Sewage treatment; Diphenylamine; Environmental science; Environmental engineering; Waste management; Environmental chemistry; Pulp and paper industry; Chemistry; Engineering","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":[0.0002925466,0.00026402,0.0001811496,0.0004708174,0.0002502602,0.0004742565,0.0001226871,0.0004101598,0.0009215487],"category_scores_gemma":[0.0003118624,0.0001531588,0.0002156572,0.0002411157,0.0002164825,0.0002725805,0.0002508515,0.0003141179,0.0001827131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000345771,"about_ca_system_score_gemma":0.0004798536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002574881,"about_ca_topic_score_gemma":0.003730791,"domain_scores_codex":[0.9998404,0.00002992152,0.00001464196,0.00003442212,0.0000359048,0.00004470069],"domain_scores_gemma":[0.9998172,0.00005340041,0.00002975192,0.00001076783,0.00006160792,0.00002727125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001541874,0.00004912825,0.008554272,0.0001326202,0.00003927483,0.000107643,0.0001879431,0.0003839913,0.9783431,0.0001280249,0.00005713724,0.01047507],"study_design_scores_gemma":[0.00001782671,0.0005091194,0.02525994,0.00001815232,0.00005445466,0.000121723,0.0002645586,0.001043359,0.9714614,0.00007201902,0.00116777,0.000009707439],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998311,0.0007251028,0.0003549463,0.00002276491,0.000007865685,0.000004481814,0.00009509302,0.000005527165,0.0004731435],"genre_scores_gemma":[0.9958669,0.0006478319,0.00043888,0.00002092291,0.00000425905,0.000008058758,0.000146976,0.000005275535,0.002860891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002574881,"threshold_uncertainty_score":0.005119741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02457314402157737,"score_gpt":0.2684376073427771,"score_spread":0.2438644633211997,"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."}}