{"id":"W2887542401","doi":"10.1016/j.envpol.2018.08.016","title":"A combined emission and receptor-based approach to modelling environmental noise in urban environments","year":2018,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Noise Effects and Management","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health; Dalhousie University; University of British Columbia; Toronto Metropolitan University","funders":"","keywords":"Noise (video); Environmental noise; Environmental science; Traffic noise; Noise control; Noise pollution; Sampling (signal processing); Computer science; Telecommunications; Noise reduction; Acoustics; Sound (geography)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004858462,0.0002955726,0.000260053,0.0001901919,0.0005714474,0.00001112116,0.0001448177,0.0001862239,0.0003908534],"category_scores_gemma":[0.000008177945,0.0002881248,0.00004827594,0.00009683378,0.000185734,0.000166724,0.0002797135,0.0003224448,0.0007691013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008806466,"about_ca_system_score_gemma":0.00001391085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007517359,"about_ca_topic_score_gemma":0.000002582936,"domain_scores_codex":[0.9976116,0.0003284125,0.0004474329,0.0006373093,0.0003703312,0.0006049415],"domain_scores_gemma":[0.9991736,0.00004096685,0.0001333523,0.0003590433,8.132674e-7,0.0002921999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001970673,0.003689038,0.166391,0.0002173418,0.00007984277,0.000009710962,0.01062259,0.01052495,0.782146,0.000654608,0.01301082,0.01068336],"study_design_scores_gemma":[0.01747893,0.002838456,0.5830728,0.0006925082,0.0001498814,0.000002307507,0.007717845,0.1677483,0.01636298,0.0004066272,0.2011119,0.002417514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778873,0.00008720271,0.01694264,0.0003584799,0.0002856437,0.001941708,0.00005726515,0.0000441919,0.002395563],"genre_scores_gemma":[0.9931725,0.0000572843,0.002388943,0.00139109,0.000214175,0.000238745,0.0001551161,0.00005013755,0.002332034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.765783,"threshold_uncertainty_score":0.9999571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139233211543106,"score_gpt":0.277462332516472,"score_spread":0.2560700004010409,"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."}}