{"id":"W2063376703","doi":"10.1089/ees.2006.0166","title":"Assessment of the Integrated ARPS–CMAQ Modeling System through Simulating PM <sub>10</sub> Concentration in Beijing, China","year":2008,"lang":"en","type":"article","venue":"Environmental Engineering Science","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"CMAQ; Beijing; Environmental science; Meteorology; Air quality index; Terrain; Climatology; Mesoscale meteorology; China; Geography; Cartography; Geology","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":[0.00256627,0.001548327,0.0008096113,0.000482226,0.0005471519,0.001050414,0.001609487,0.0009113356,0.0007264422],"category_scores_gemma":[0.002357294,0.0006329546,0.0006641596,0.0005130767,0.0003811911,0.001028347,0.0008654638,0.0005322247,0.0001432323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002147306,"about_ca_system_score_gemma":0.002610899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1373274,"about_ca_topic_score_gemma":0.07034396,"domain_scores_codex":[0.9992667,0.0002915858,0.00005764193,0.0001469548,0.000153776,0.0000833399],"domain_scores_gemma":[0.9990109,0.0002821255,0.00009649024,0.00009670119,0.0003967737,0.0001169656],"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":[0.0002064501,0.0002839252,0.04256654,0.00006707,0.0001665007,0.0001615956,0.00009254521,0.9409965,0.002493636,0.0003860381,0.0007073772,0.01187187],"study_design_scores_gemma":[0.00004355315,0.00005557708,0.005393131,0.000003395255,0.00003248861,0.000004833105,0.00003234698,0.9937155,0.0005290139,0.00003994341,0.0001410994,0.000009103251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896081,0.0001910035,0.006633609,0.0002350992,0.00004453694,0.00009594536,0.0005024789,0.0006389178,0.002050377],"genre_scores_gemma":[0.9936626,0.0000619369,0.005272884,0.00003016627,0.000008104156,0.00005491127,0.0005654194,0.00003221255,0.0003117757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1373274,"threshold_uncertainty_score":0.2730562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007149169393830504,"score_gpt":0.1832228730306315,"score_spread":0.1760737036368009,"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."}}