{"id":"W2410632389","doi":"10.1038/jes.2015.82","title":"Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics","year":2016,"lang":"en","type":"article","venue":"Journal of Exposure Science & Environmental Epidemiology","topic":"Noise Effects and Management","field":"Health Professions","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de Santé Publique du Québec; McGill University; Université de Montréal","funders":"","keywords":"Noise (video); Environmental science; Environmental noise; Sampling (signal processing); Spatial variability; Noise exposure; Traffic noise; Statistics; Meteorology; Physical geography; Geography; Mathematics; Computer science; Telecommunications; Noise reduction; Geology","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":[0.006179824,0.0001571672,0.0006105023,0.00008852304,0.0002049836,0.000002470627,0.0002976682,0.00009276283,0.0001146283],"category_scores_gemma":[0.00207394,0.00008948206,0.00005384711,0.00006245345,0.0007911988,0.0002309514,0.0003175195,0.0002814378,6.489953e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009322431,"about_ca_system_score_gemma":0.0003814885,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04308309,"about_ca_topic_score_gemma":0.01388786,"domain_scores_codex":[0.9959593,0.001391737,0.001466168,0.0002654977,0.0004881802,0.0004291623],"domain_scores_gemma":[0.9969935,0.001406379,0.001122529,0.0002792549,0.0000226267,0.000175677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009646161,0.00009051734,0.8876035,0.00002690224,0.00001458025,0.000004008469,0.0001494326,0.001280001,0.1089189,0.0000256527,0.0000123438,0.001777757],"study_design_scores_gemma":[0.0008981695,0.000154997,0.9943204,0.0001590735,0.00004177694,0.000007736649,0.0001222167,0.003669269,0.0002251496,0.0002931352,0.00002132428,0.00008675805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919704,0.00004823668,0.006723917,0.0002014435,0.0004504922,0.0003438438,0.0002450033,9.453942e-7,0.00001575708],"genre_scores_gemma":[0.9985992,0.00007298965,0.001058546,0.0001894461,0.00005520791,0.000002637518,9.966587e-7,0.000008677044,0.00001232129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1086937,"threshold_uncertainty_score":0.9632891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09532725621974374,"score_gpt":0.3470412001125505,"score_spread":0.2517139438928067,"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."}}