{"id":"W2161092904","doi":"10.1139/s03-084","title":"Modeling of hourly NO<sub><i>x</i></sub> concentrations using artificial neural networks","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Environmental Protection Agency","keywords":"Artificial neural network; Mean squared error; Air quality index; Computer science; Nonlinear system; Environmental science; Stack (abstract data type); Component (thermodynamics); Meteorology; Data mining; Machine learning; Statistics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005089041,0.0001116625,0.0001521893,0.00005666489,0.0001794331,0.0000356306,0.000166776,0.00003747477,0.000007581951],"category_scores_gemma":[0.00005431521,0.0001049306,0.000054452,0.0001774793,0.000341732,0.000481105,0.0000892009,0.0001957169,0.00000176184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969807,"about_ca_system_score_gemma":0.00001580707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001749949,"about_ca_topic_score_gemma":6.277704e-7,"domain_scores_codex":[0.9987768,0.00001033505,0.0003829305,0.0001519447,0.0004189322,0.0002590691],"domain_scores_gemma":[0.9995719,0.00002188536,0.0001495188,0.00008853798,0.000007644559,0.0001605627],"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":[0.000004436242,0.00002210262,0.0006499406,0.000001941978,0.000002146176,0.000003014717,0.0001100334,0.6232336,0.3749177,0.00001796088,2.577419e-7,0.00103681],"study_design_scores_gemma":[0.0001627511,0.0001113798,0.002706631,0.00004478913,0.00001402182,0.00008844034,0.0001012023,0.9656432,0.03096691,0.00004449574,0.000003252451,0.0001129232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9304278,0.00008561141,0.06909756,0.00001618846,0.0003068183,0.00003679088,0.00000269908,0.000006577642,0.00001997958],"genre_scores_gemma":[0.9949363,0.00003497538,0.004876407,0.00001063204,0.0001316764,3.342335e-7,2.665708e-7,0.000008650704,7.527349e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3439508,"threshold_uncertainty_score":0.4278944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01681518344538366,"score_gpt":0.2116734937342438,"score_spread":0.1948583102888601,"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."}}