{"id":"W4322774083","doi":"10.1016/j.dib.2023.109022","title":"2002–2017 anthropogenic emissions data for air quality modeling over the United States","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Environment and Climate Change Canada; Office of Research and Development; U.S. Environmental Protection Agency","keywords":"Air quality index; Environmental science; Air pollution; Pollutant; Emission inventory; Criteria air contaminants; Meteorology; Scale (ratio); Data quality; Air pollutants; Environmental resource management; Geography; Engineering; Cartography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009628322,0.0001058261,0.0001292031,0.000006863259,0.0002747266,0.00005648189,0.001638242,0.00005334265,0.001384314],"category_scores_gemma":[0.0004505445,0.00007329772,0.00001877728,0.0004453597,0.0001016589,0.0003783064,0.0002868977,0.0001366189,0.00009718441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002373765,"about_ca_system_score_gemma":0.00006095571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034608,"about_ca_topic_score_gemma":0.001941132,"domain_scores_codex":[0.9987881,0.00005318968,0.0002474531,0.0004357748,0.0001759996,0.0002995188],"domain_scores_gemma":[0.9974961,0.0004940326,0.00006065216,0.001856198,0.00001731831,0.00007569107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001456038,0.00006920977,0.06498845,0.0001473167,0.00007809687,0.00003192313,0.0004760815,0.2532845,0.0002161176,0.00004021902,0.6722103,0.008312183],"study_design_scores_gemma":[0.0002072502,0.000006701375,0.02302195,0.00001842594,0.00000899025,0.00000192274,0.0005159166,0.8269671,0.00001662619,0.0001685586,0.1489466,0.0001199389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9439325,0.0003665907,0.002868359,0.001942947,0.0002349451,0.0003029718,0.04984844,0.0001057514,0.0003974924],"genre_scores_gemma":[0.7517249,0.0009768051,0.001503999,0.0009958779,0.0002959042,0.00000306845,0.243477,0.00001042936,0.001011949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5736826,"threshold_uncertainty_score":0.9995285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1536010540214039,"score_gpt":0.3576647089613512,"score_spread":0.2040636549399472,"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."}}