{"id":"W3165514185","doi":"10.1039/d1ew00203a","title":"Comprehensive evaluation of non-catalytic wet air oxidation as a pretreatment to remove pharmaceuticals from hospital effluents","year":2021,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wet oxidation; Effluent; Wastewater; Catalysis; Chemical oxygen demand; Waste management; Catalytic oxidation; Chemistry; Environmental chemistry; Environmental science; Environmental engineering; Organic 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001420382,0.0002904313,0.0003118785,0.0002700274,0.0004134322,0.00005083305,0.0009017246,0.0001565303,0.004952181],"category_scores_gemma":[0.0001944716,0.0002311075,0.00007904234,0.001093359,0.003663547,0.0005598962,0.002797848,0.0004237978,0.00660442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001943431,"about_ca_system_score_gemma":0.00006490528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002573927,"about_ca_topic_score_gemma":0.000008667301,"domain_scores_codex":[0.9937038,0.0002427082,0.0004280519,0.001217672,0.003200559,0.001207253],"domain_scores_gemma":[0.9984744,0.00006618593,0.00007049402,0.0007813119,0.00003629128,0.0005713592],"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":[0.0000319605,0.0009114487,0.0163593,0.00000544913,0.00002864449,0.00005874844,0.000497811,0.0005138326,0.9608992,0.000008784831,0.0000926658,0.02059214],"study_design_scores_gemma":[0.0008981628,0.0004127504,0.09818532,0.00003524352,0.00004489852,0.00002430947,0.000772776,0.001180186,0.8946049,0.001771254,0.001821982,0.0002482459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930904,0.0001006525,0.00005652914,0.003094725,0.0001321309,0.001196669,0.00004179018,0.00003703411,0.002250051],"genre_scores_gemma":[0.9981378,0.00007947442,0.001036933,0.0002936908,0.00002227795,0.00006512379,0.00006155565,0.00002370104,0.0002793886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08182602,"threshold_uncertainty_score":0.9990479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05774626845194861,"score_gpt":0.3988135369168805,"score_spread":0.3410672684649319,"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."}}