{"id":"W2920970828","doi":"10.3390/s19061415","title":"The Life of a New York City Noise Sensor Network","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Noise Effects and Management","field":"Health Professions","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Center for Urban Science and Progress; National Science Foundation","keywords":"Noise (video); Wireless sensor network; Telecommunications; Computer science; Engineering; Computer network; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056796,0.0003813421,0.0002457504,0.0003197744,0.0008408778,0.001034765,0.0005045509,0.00045285,0.006298492],"category_scores_gemma":[0.001429116,0.0001856931,0.0001586932,0.0003638664,0.0002424066,0.001434306,0.0007123235,0.0004315022,0.001110033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010851,"about_ca_system_score_gemma":0.001393906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05928639,"about_ca_topic_score_gemma":0.1006336,"domain_scores_codex":[0.9997289,0.00005242908,0.00001143774,0.00004813913,0.0001204755,0.00003860245],"domain_scores_gemma":[0.9993683,0.0001036293,0.00003932677,0.00008281703,0.0003162469,0.00008965952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005382088,0.0003633767,0.06220084,0.0004007929,0.0001833936,0.000964733,0.000895769,0.2209833,0.0304018,0.02799812,0.2082814,0.4467882],"study_design_scores_gemma":[0.00005385256,0.0005927916,0.02274342,0.0001310484,0.0001000199,0.0005158132,0.001110399,0.7653586,0.007532068,0.00515724,0.1966205,0.00008429081],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5498057,0.004454458,0.2610078,0.0236759,0.001597619,0.001174764,0.005531977,0.007917525,0.1448344],"genre_scores_gemma":[0.8484371,0.002591745,0.06517127,0.0007351221,0.0001492569,0.00047508,0.00413576,0.0001296679,0.07817499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05928639,"threshold_uncertainty_score":0.1178826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04832447729469935,"score_gpt":0.3477048604344218,"score_spread":0.2993803831397225,"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."}}