{"id":"W2906021381","doi":"10.3390/data4010002","title":"A Mobile Air Pollution Monitoring Data Set","year":2018,"lang":"en","type":"article","venue":"Data","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministère de l’Environnement, de la Protection de la nature et des Parcs","keywords":"Environmental science; Nitrogen dioxide; Air pollution; Pollution; Pollutant; Particulates; Sampling (signal processing); Ozone; Meteorology; Computer science; Geography; Chemistry; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003891339,0.001204387,0.0008351623,0.002112655,0.001762402,0.001151822,0.001936896,0.0009981338,0.00518972],"category_scores_gemma":[0.001792663,0.000400551,0.0006455508,0.006792332,0.0005413902,0.0004697776,0.0008906089,0.0007628658,0.00356225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01038057,"about_ca_system_score_gemma":0.01494233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9622517,"about_ca_topic_score_gemma":0.9798902,"domain_scores_codex":[0.9990439,0.00004890967,0.00006941881,0.0002125413,0.0004364371,0.0001887645],"domain_scores_gemma":[0.9977292,0.0001139188,0.000122323,0.0002089182,0.00161097,0.0002146007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000506908,0.0002747744,0.1256481,0.001604283,0.0003431141,0.0007386446,0.0006319092,0.009689607,0.003632414,0.001878314,0.8240397,0.03101213],"study_design_scores_gemma":[0.0002231314,0.00008985001,0.4174035,0.0003486612,0.0001424266,0.0001989207,0.001143851,0.01310089,0.00294945,0.0006408104,0.5635899,0.0001686114],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01782839,0.0002077118,0.0003684204,0.0001413018,0.00002924227,0.0001266352,0.9783627,0.0002715383,0.0026641],"genre_scores_gemma":[0.02932713,0.0001672271,0.00114817,0.0000714923,0.00001214782,0.0001624449,0.9667967,0.00002428013,0.002290446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9622517,"threshold_uncertainty_score":0.07594121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2196030125958326,"score_gpt":0.4140672553854713,"score_spread":0.1944642427896386,"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."}}