{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000621249,0.00006015819,0.00006258287,0.00000968327,0.0001856717,0.00001648549,0.001264101,0.00003964327,0.0009117681],"category_scores_gemma":[0.0001008855,0.00005548835,0.0000041607,0.0001155445,0.0001353219,0.0009237317,0.002387132,0.00007460846,0.003298213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005763373,"about_ca_system_score_gemma":0.000017353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252384,"about_ca_topic_score_gemma":0.0002118172,"domain_scores_codex":[0.9990588,0.00004082806,0.0001210495,0.0003471388,0.0001930128,0.0002391128],"domain_scores_gemma":[0.9975909,0.00001521443,0.00004206671,0.002224106,0.000002392555,0.0001253036],"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":[0.00001617125,0.00004647825,0.01312704,0.00001549973,0.000004392281,0.000002275115,0.0007361937,0.00002081348,0.0002831024,0.00001826358,0.935001,0.05072883],"study_design_scores_gemma":[0.000105202,0.00007813785,0.05654682,0.00001420876,0.000005554178,0.000003392036,0.0001446014,0.003642249,0.0002310762,0.0000891001,0.9390539,0.00008573659],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8846101,0.0008576839,0.01868676,0.01853512,0.004185767,0.001940944,0.03569238,0.0007322227,0.03475909],"genre_scores_gemma":[0.9880745,0.00008013931,0.006250363,0.002429224,0.0006751766,0.000003978104,0.002107671,0.000009529773,0.0003694649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1034644,"threshold_uncertainty_score":0.9983221,"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."}}