{"id":"W2953845057","doi":"10.1061/(asce)he.1943-5584.0001814","title":"Quantifying Rainfall-Derived Inflow from Private Foundation Drains in Sanitary Sewers: Case Study in London, Ontario, Canada","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Institute for Catastrophic Loss Reduction","keywords":"Inflow; Sanitary sewer; Hydrology (agriculture); Environmental science; Foundation (evidence); Infiltration (HVAC); Subdivision; Bootstrapping (finance); Environmental engineering; Civil engineering; Geotechnical engineering; Geography; Mathematics; Engineering; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000459134,0.0001503351,0.0002462428,0.0001605106,0.0000341937,0.00002645679,0.0002162728,0.00004281648,0.000494705],"category_scores_gemma":[0.00005047855,0.0001422537,0.00003673886,0.0002377149,0.00001379194,0.000485043,0.0001406763,0.0003566605,0.00001373257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796907,"about_ca_system_score_gemma":0.00005246238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8187004,"about_ca_topic_score_gemma":0.9749478,"domain_scores_codex":[0.9986914,0.00005219929,0.0005086185,0.0001833682,0.0002902076,0.0002742194],"domain_scores_gemma":[0.9995102,0.00008076551,0.0001672851,0.0001714015,0.000006951365,0.00006340324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001033595,0.00005205352,0.5862588,0.000003011846,0.00001959716,0.0020978,0.001410389,0.4067831,0.003289504,9.764291e-7,0.00002960637,0.00004486565],"study_design_scores_gemma":[0.001617953,0.0002028843,0.9442628,0.00004422339,0.00002498701,0.0001958129,0.0009794171,0.05117553,0.00007070465,0.00002317173,0.00115966,0.0002428261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988158,0.000027542,0.0003390865,0.00006946772,0.0003274189,0.0002954912,8.303649e-7,0.00001166337,0.0001127346],"genre_scores_gemma":[0.9987445,0.00000316489,0.00111174,0.00006927914,0.00002315283,0.000004836155,0.00000252299,0.00001148364,0.00002929217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3580041,"threshold_uncertainty_score":0.5800937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447944636488175,"score_gpt":0.2059911070258136,"score_spread":0.1915116606609318,"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."}}