{"id":"W4246281393","doi":"10.5194/amt-2016-200","title":"A mobile sensor network to map carbon dioxide emissions in urban environments","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Environmental science; Transect; Mixing ratio; Carbon dioxide; Atmospheric sciences; Mixing (physics); Gas analyzer; Greenhouse gas; Plume; Meteorology; Remote sensing; Geography; Environmental chemistry; Geology; Physics; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002679325,0.0005095597,0.0002728329,0.0007921442,0.0002028569,0.0003373284,0.0003630461,0.0002534213,0.001032702],"category_scores_gemma":[0.0003997519,0.0001243841,0.000149507,0.0007168524,0.00008325244,0.0005213239,0.0003567789,0.0001787431,0.0003210041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003361125,"about_ca_system_score_gemma":0.00028065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006033977,"about_ca_topic_score_gemma":0.009467957,"domain_scores_codex":[0.9998312,0.00003386676,0.000007661537,0.00004482489,0.00006639452,0.00001604351],"domain_scores_gemma":[0.9998115,0.00003492939,0.00001833424,0.00002200338,0.00009806237,0.00001523123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000807032,0.0004311363,0.05915653,0.0004872272,0.0003099241,0.0005748968,0.0002247992,0.1914685,0.1323643,0.004337435,0.01405245,0.5957857],"study_design_scores_gemma":[0.00007001583,0.000354673,0.02901522,0.00003435096,0.00006646494,0.0001783411,0.0001664833,0.9282982,0.02115576,0.001494042,0.01912856,0.00003778087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4961092,0.001257878,0.4793945,0.0004052802,0.0003458408,0.0005933762,0.004651624,0.006100344,0.01114202],"genre_scores_gemma":[0.8403608,0.0004302349,0.1528702,0.00007980631,0.00003438632,0.0003809883,0.002175161,0.00005343552,0.003614996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006033977,"threshold_uncertainty_score":0.01199776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0202463869371699,"score_gpt":0.2571432434640812,"score_spread":0.2368968565269113,"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."}}