{"id":"W4417259545","doi":"10.1016/j.buildenv.2025.114127","title":"An air quality digital twin for real-time outdoor air quality monitoring and prediction","year":2025,"lang":"en","type":"article","venue":"Building and Environment","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Air quality index; Quality (philosophy); Air pollution; Air monitoring; Air temperature","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001041651,0.0002154316,0.0002464947,0.0000357073,0.0004356779,0.00007012484,0.0001231161,0.0001144567,0.00001685718],"category_scores_gemma":[0.00007788593,0.0002143524,0.00005774207,0.00006344326,0.0002042701,0.0003686682,0.0002033975,0.0001184856,0.00001120731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395724,"about_ca_system_score_gemma":0.000005187609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003621587,"about_ca_topic_score_gemma":9.788398e-7,"domain_scores_codex":[0.9982722,0.00009242376,0.0004173862,0.0006161703,0.0002584658,0.0003433571],"domain_scores_gemma":[0.9992107,0.0001949252,0.0001174116,0.0003068995,0.000003310378,0.0001667498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001317106,0.000249666,0.7737681,0.000122639,0.0000481599,0.000001156621,0.001121822,0.005986769,0.07527383,0.00027717,0.0001934874,0.1428255],"study_design_scores_gemma":[0.0006751359,0.0001894162,0.9825237,0.00008242679,0.00003718813,0.000002339189,0.0006799119,0.002490411,0.006566438,0.001346765,0.005060496,0.000345765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923863,0.00005830795,0.005924767,0.0002715316,0.0001811541,0.0002705333,0.00005899944,0.000121427,0.0007269168],"genre_scores_gemma":[0.9926326,0.00008827625,0.006051801,0.00002308311,0.0001669023,0.00005190232,0.00001502124,0.00001768883,0.0009527524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2087556,"threshold_uncertainty_score":0.8741038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507609659031533,"score_gpt":0.2979068614083893,"score_spread":0.2728307648180739,"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."}}