{"id":"W3022013332","doi":"10.2166/wst.2020.217","title":"Statistical analysis of sewer odour based on 10-year complaint data","year":2020,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Anhui Province","keywords":"Sanitary sewer; Complaint; Environmental science; Storm; Combined sewer; Environmental engineering; Annoyance; Civil engineering; Hydrology (agriculture); Engineering; Forensic engineering; Stormwater; Geotechnical engineering; Geography; Meteorology; Medicine; Surface runoff","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499061,0.0001274983,0.000346164,0.0008152108,0.00007330257,0.00001960771,0.001870853,0.0001478114,0.0006086575],"category_scores_gemma":[0.0009755568,0.00008624596,0.00004056469,0.002340565,0.0007519654,0.00008559412,0.0007536159,0.000246673,0.00009406927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003331677,"about_ca_system_score_gemma":0.00002988759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006960882,"about_ca_topic_score_gemma":0.000001109149,"domain_scores_codex":[0.9984525,0.000008090812,0.0002548896,0.0005579741,0.0003318166,0.0003947224],"domain_scores_gemma":[0.9987032,0.0000583494,0.00003950403,0.00102736,0.00007863493,0.00009297463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007720352,0.00009199236,0.003276672,0.0000265805,0.00009289468,0.00003088458,0.00007955307,0.01472144,0.9601197,0.0144967,0.001090698,0.005895702],"study_design_scores_gemma":[0.000229384,0.0001069991,0.0001711512,0.000006577667,0.00008135315,5.761773e-7,0.00007889964,0.6654173,0.3294133,0.000217237,0.004157982,0.0001193197],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3912721,0.00003147015,0.5694617,0.03235354,0.0001650391,0.0002923671,0.0004273035,0.002566567,0.00342984],"genre_scores_gemma":[0.9924102,0.000001024917,0.007320344,0.0001791731,0.00000949437,0.000004371687,0.00003740443,0.000007854776,0.00003016349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6506958,"threshold_uncertainty_score":0.6664373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05206620042504085,"score_gpt":0.2820247357825578,"score_spread":0.2299585353575169,"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."}}