{"id":"W4407874876","doi":"10.3390/atmos16030255","title":"Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas","year":2025,"lang":"en","type":"article","venue":"Atmosphere","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Air quality index; Ozone; Environmental science; Meteorology; Architecture; Quality (philosophy); Computer science; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009468082,0.0001989764,0.0002656845,0.000004440676,0.0005506774,0.00003431277,0.0002981111,0.00005994926,0.00002339669],"category_scores_gemma":[0.0004087247,0.0001546482,0.0001803419,0.0001759566,0.0001880433,0.00009459169,0.0002047392,0.0002305231,0.000002390763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000135689,"about_ca_system_score_gemma":0.00002220471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000110403,"about_ca_topic_score_gemma":0.00003706395,"domain_scores_codex":[0.9983684,0.00009885404,0.0005112215,0.0003863674,0.0002384052,0.0003967024],"domain_scores_gemma":[0.9986075,0.000795368,0.000241165,0.0002822191,0.00002885546,0.00004491244],"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":[0.0001655774,0.00007349918,0.007347343,0.0002603875,0.00006957813,7.925346e-7,0.0007089532,0.9358906,0.000156256,0.005642037,0.0008561321,0.04882885],"study_design_scores_gemma":[0.0006094578,0.00008797595,0.0007711697,0.0001644974,0.00007335915,0.000002143716,0.0004346023,0.9902875,0.0004609465,0.004013133,0.002913602,0.0001816406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1741348,0.0001522346,0.8227791,0.0007943472,0.0001119807,0.0009166731,0.0000355896,0.00007004797,0.001005233],"genre_scores_gemma":[0.8731182,0.00001042638,0.1237627,0.0001422302,0.00005336016,0.0001568979,0.00001889958,0.00002200853,0.00271536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6990165,"threshold_uncertainty_score":0.6306369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346823459740648,"score_gpt":0.2798458004912149,"score_spread":0.2463775658938085,"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."}}