{"id":"W2990025184","doi":"10.1289/isesisee.2018.p02.1920","title":"Ultrafine and Fine Particulate Matter Levels over Resurfacing Operations at Skating Arenas","year":2018,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; University of Toronto; Public Health Ontario","funders":"","keywords":"Ultrafine particle; Particulates; Environmental science; Population; Particle number; Meteorology; Atmospheric sciences; Engineering; Geology; Geography; Chemistry; Chemical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728223,0.0005194358,0.0003034047,0.0006439685,0.0007054089,0.0006290475,0.0003070102,0.000343818,0.00221708],"category_scores_gemma":[0.0006104768,0.0002191989,0.0004107487,0.0003262199,0.0004434879,0.0004572277,0.0006139933,0.0003537819,0.0005208495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008154025,"about_ca_system_score_gemma":0.0005600092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01672545,"about_ca_topic_score_gemma":0.05294048,"domain_scores_codex":[0.9996783,0.00003688509,0.0000136959,0.0001000471,0.00007834733,0.00009280421],"domain_scores_gemma":[0.9993039,0.00005733765,0.0002212655,0.00002058289,0.0002336824,0.000163157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001620406,0.001098919,0.9450714,0.0001813232,0.0001340637,0.0005680643,0.003111445,0.0006354473,0.02825687,0.00003753509,0.0004803451,0.01880398],"study_design_scores_gemma":[0.00000578384,0.0014149,0.9947516,0.0000218385,0.00002445026,0.00007059753,0.001501558,0.000306162,0.001511277,0.00001860354,0.0003621796,0.00001110407],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994317,0.00002510998,0.00008210631,0.000006715169,0.00000186783,0.00001521698,0.0001389293,0.000007101776,0.0002913124],"genre_scores_gemma":[0.9982651,0.0000550826,0.0004372976,0.00001530035,0.000004342129,0.00003392738,0.0003397169,0.000004609336,0.0008448403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01672545,"threshold_uncertainty_score":0.03325617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02406662150856368,"score_gpt":0.2498372180597959,"score_spread":0.2257705965512322,"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."}}