{"id":"W2733809378","doi":"10.1016/j.envpol.2017.06.071","title":"Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors","year":2017,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Environmental science; Sampling (signal processing); Air pollution; Nitrogen dioxide; Pollution; Global Positioning System; Term (time); Computer science; Remote sensing; Meteorology; Geography; Telecommunications","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":[0.001299978,0.0007092516,0.0004891225,0.0004024979,0.0001989457,0.0008355603,0.000632891,0.0009963831,0.00090219],"category_scores_gemma":[0.006141182,0.0005447761,0.0007690141,0.000524217,0.0003410724,0.000812959,0.0005348325,0.0008335353,0.0001807085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581186,"about_ca_system_score_gemma":0.0005191406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02616812,"about_ca_topic_score_gemma":0.02133501,"domain_scores_codex":[0.9996487,0.0001085706,0.00001607341,0.0001200432,0.00003660468,0.00007000735],"domain_scores_gemma":[0.99705,0.002385499,0.0002214133,0.0001873422,0.0001060726,0.00004961125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008846975,0.0001210967,0.03772003,0.00005009718,0.0001847002,0.00008244692,0.00004939044,0.9438804,0.005871852,0.0006693653,0.0002771548,0.01100492],"study_design_scores_gemma":[0.000002903381,0.0000209374,0.009981507,0.000002048154,0.00001703149,0.00001236377,0.00001852594,0.9884299,0.001046139,0.0003892099,0.00007243267,0.000006915804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968064,0.000140121,0.03046147,0.0001789409,0.00003160805,0.00001524974,0.0002967888,0.0001826342,0.0006291177],"genre_scores_gemma":[0.9977911,0.00005560898,0.001526908,0.00001806348,0.00001150497,0.000007865174,0.0002094874,0.00001556213,0.000363983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02616812,"threshold_uncertainty_score":0.05203164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07121820323209441,"score_gpt":0.3132350729866679,"score_spread":0.2420168697545735,"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."}}