{"id":"W4379390296","doi":"10.32920/23296100.v1","title":"Road Traffic Noise Modelling and Population Exposure Assessments for Large Municipalities in Ontario","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Saskatchewan; Statistics Canada","funders":"","keywords":"Noise (video); Traffic noise; Christian ministry; Road traffic; Legislation; Population; Noise exposure; Environmental health; Geography; Environmental science; Transport engineering; Computer science; Engineering; Medicine; Political science; Audiology; Noise reduction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004068057,0.0002587134,0.0001980956,0.0003963953,0.000934381,0.00058671,0.0005470573,0.0002988421,0.001397535],"category_scores_gemma":[0.001053174,0.0002067899,0.0004533979,0.0008542512,0.0003013383,0.0002738167,0.0005106415,0.0001812887,0.0002790252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01297589,"about_ca_system_score_gemma":0.009853549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9680186,"about_ca_topic_score_gemma":0.9814962,"domain_scores_codex":[0.9997149,0.00005602086,0.00001312436,0.00005733088,0.00009626575,0.00006232758],"domain_scores_gemma":[0.9996351,0.00006637504,0.0000502087,0.0000332085,0.0001800546,0.00003505132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003791272,0.0002886918,0.8167179,0.0001875779,0.0001343792,0.0006074401,0.00607046,0.1178964,0.004241208,0.00124436,0.003689196,0.04854331],"study_design_scores_gemma":[0.00003659197,0.0001323617,0.8469904,0.00004016485,0.00007806665,0.0000745842,0.008373265,0.1354477,0.00124476,0.0004576267,0.007081298,0.00004317829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955235,0.00004024829,0.000989955,0.00008529848,0.000001949062,0.00003934478,0.0008697367,0.00003735962,0.00241281],"genre_scores_gemma":[0.9962928,0.00007278335,0.000847209,0.00001104617,0.000001351674,0.00003246974,0.0007282734,0.000009677136,0.00200449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03198141,"threshold_uncertainty_score":0.09414715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1887111650490342,"score_gpt":0.4445135244596563,"score_spread":0.2558023594106221,"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."}}