{"id":"W4387097696","doi":"10.1038/s41612-023-00475-3","title":"Improving air quality assessment using physics-inspired deep graph learning","year":2023,"lang":"en","type":"article","venue":"npj Climate and Atmospheric Science","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Chinese Academy of Sciences; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science; State Key Laboratory of Resources and Environmental Information System; National Natural Science Foundation of China; Nvidia","keywords":"Extrapolation; Air quality index; Artificial neural network; Pollutant; Graph; Computer science; Air pollution; Reliability (semiconductor); Air pollutants; Artificial intelligence; Machine learning; Environmental science; Data mining; Meteorology; Mathematics; Statistics; Geography; Physics; Theoretical computer science","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":[0.0005044867,0.0009029343,0.0005601686,0.0009781142,0.000228383,0.0004790538,0.001066995,0.0007520209,0.0013347],"category_scores_gemma":[0.001628619,0.0002325016,0.0005325929,0.0004859813,0.0003467686,0.0009743604,0.00083118,0.0008344956,0.0002020768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007469378,"about_ca_system_score_gemma":0.0007478829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01860901,"about_ca_topic_score_gemma":0.02231631,"domain_scores_codex":[0.9998196,0.00004290756,0.000007083019,0.00005693377,0.00004717429,0.00002640278],"domain_scores_gemma":[0.9994162,0.0002775099,0.00007062672,0.00005614477,0.000138962,0.00004059226],"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":[0.00005534775,0.0001226883,0.003815942,0.00004949492,0.00006370869,0.00003637157,0.00002114321,0.9211735,0.004908377,0.001521285,0.0010707,0.06716151],"study_design_scores_gemma":[0.000002092002,0.000005080301,0.0001395103,7.694651e-7,0.000001993788,0.000001186158,0.000001343173,0.9989838,0.000320144,0.0005073707,0.00003534805,0.000001384236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2409251,0.0004744728,0.7511888,0.0005326215,0.00009813131,0.00006178267,0.0004042526,0.003543586,0.00277123],"genre_scores_gemma":[0.9369476,0.00007119842,0.06149375,0.0001436303,0.00002685234,0.00001776074,0.0003149181,0.00006629167,0.0009180342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01860901,"threshold_uncertainty_score":0.03700143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04431416067510092,"score_gpt":0.3195652019720248,"score_spread":0.2752510412969238,"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."}}