{"id":"W4412514728","doi":"10.1007/978-981-96-9875-2_24","title":"AGTCN: An Adaptive Gating Approach in Spatiotemporal Convolutional Networks for Accurate Air Quality Prediction","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Gating; Convolutional neural network; Artificial intelligence; Machine learning","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":[0.0005101254,0.0008808774,0.0005157755,0.0003785857,0.0002509639,0.0005615229,0.001778628,0.00090197,0.002479187],"category_scores_gemma":[0.001184318,0.0004126604,0.0005076961,0.000675416,0.0002997749,0.0009396086,0.0009262084,0.001415232,0.0007308811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007037974,"about_ca_system_score_gemma":0.0009870201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02614296,"about_ca_topic_score_gemma":0.03522796,"domain_scores_codex":[0.9998643,0.00001989005,0.000007164208,0.00004634217,0.00003803078,0.00002419035],"domain_scores_gemma":[0.999738,0.0001032424,0.00001710844,0.00004282124,0.00008191761,0.0000168563],"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":[0.0001881201,0.0001217485,0.000987026,0.00007436241,0.0001066448,0.00009487515,0.00003469381,0.5979002,0.01035272,0.009632816,0.01617486,0.364332],"study_design_scores_gemma":[0.000002207616,0.000005317541,0.00006096431,0.000001862342,0.000003839175,0.000004421891,9.91707e-7,0.9974667,0.00083537,0.00113817,0.0004781535,0.000001987426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02300337,0.0008172722,0.966859,0.0002849958,0.000345794,0.00004862088,0.0008015472,0.005123395,0.002715937],"genre_scores_gemma":[0.5154805,0.001041628,0.4641071,0.0004767744,0.0002516866,0.0001750613,0.003350381,0.0008769433,0.01423986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02614296,"threshold_uncertainty_score":0.05198157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05146765911704951,"score_gpt":0.2895836680341801,"score_spread":0.2381160089171306,"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."}}