{"id":"W2089951027","doi":"10.2166/wst.2012.562","title":"Modelling micro-pollutant fate in wastewater collection and treatment systems: status and challenges","year":2012,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Windsor; Hydromantis Environmental Software Solutions (Canada)","funders":"","keywords":"Pollutant; Environmental science; Wastewater; Complement (music); Effluent; Pollution; Environmental planning; Heuristic; Sewage treatment; Risk analysis (engineering); Computer science; Biochemical engineering; Environmental engineering; Environmental economics; Business; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.000255057,0.0001388975,0.0001433253,0.0001986543,0.000169626,0.00003337429,0.0001049966,0.00007826446,0.00002379843],"category_scores_gemma":[0.000003537326,0.00008652562,0.000009122306,0.000251604,0.001140379,0.0004317054,0.0002865098,0.00007915232,0.00005836973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003152977,"about_ca_system_score_gemma":0.000003941457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002944837,"about_ca_topic_score_gemma":0.0000189178,"domain_scores_codex":[0.9985873,0.00002037463,0.000142476,0.0003410432,0.000135809,0.0007730105],"domain_scores_gemma":[0.999649,0.000006752279,0.00002198454,0.0001369935,0.000001474391,0.0001837839],"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":[0.00001289736,0.0001852535,0.1352186,0.00001423362,0.000004445395,0.000008661897,0.00343421,0.00220356,0.8493718,0.0001856999,0.000001144619,0.009359451],"study_design_scores_gemma":[0.0007995748,0.0003304789,0.01789497,0.00003679461,0.00001754937,0.0001790503,0.002088784,0.02414246,0.9480988,0.0004823435,0.00556066,0.0003685682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966497,0.001592306,0.00005088535,0.000958151,0.0000976538,0.0001913641,0.000001624302,0.00003873929,0.0004196141],"genre_scores_gemma":[0.9950877,0.004306073,0.0004385633,0.00002343048,0.000008369865,0.000008783884,3.394888e-7,0.000006558299,0.0001201872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1173237,"threshold_uncertainty_score":0.4201774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04019707384563084,"score_gpt":0.2527092988128178,"score_spread":0.212512224967187,"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."}}