{"id":"W2791143128","doi":"10.1080/15459624.2018.1442006","title":"Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada","year":2018,"lang":"en","type":"article","venue":"Journal of Occupational and Environmental Hygiene","topic":"Noise Effects and Management","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; Public Health Ontario","funders":"Public Health Ontario","keywords":"Geocoding; Noise (video); Environmental science; Traffic noise; Noise pollution; Meteorology; Noise exposure; Geography; Medicine; Remote sensing; Computer science; Noise reduction","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":[0.0001914331,0.0001214717,0.0002058587,0.00002308572,0.000421189,0.000005726196,0.00008166858,0.00003472313,0.0002847064],"category_scores_gemma":[0.00001607126,0.00009580815,0.00004826819,0.00002541015,0.00006431786,0.0003314341,0.00006270965,0.0001171186,0.000005607518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005692287,"about_ca_system_score_gemma":0.0001635686,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009811659,"about_ca_topic_score_gemma":0.01842941,"domain_scores_codex":[0.9988748,0.00007368526,0.0003802413,0.0001357038,0.0003090363,0.0002264972],"domain_scores_gemma":[0.9992563,0.0001919239,0.0002736141,0.00007595825,0.00006011024,0.0001420851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.03485852,0.003091954,0.1857887,0.002087853,0.003913731,0.0002315391,0.03745428,0.03578219,0.09269066,0.002009814,0.3216624,0.2804284],"study_design_scores_gemma":[0.002455585,0.001129133,0.9107453,0.0001443618,0.0000837935,0.00001180495,0.005998422,0.002766944,0.0003105081,0.00002487706,0.07613082,0.0001984119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972659,0.0004101771,0.0003974481,0.0002161489,0.0009011196,0.0004000227,0.0001289522,0.000005294182,0.0002749315],"genre_scores_gemma":[0.9973404,0.0002206871,0.0008296345,0.0003822,0.0006578396,0.00002594996,0.00002547722,0.00001271458,0.0005051303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7249567,"threshold_uncertainty_score":0.9994817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08299016663522252,"score_gpt":0.3398194111471757,"score_spread":0.2568292445119532,"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."}}