{"id":"W4224292942","doi":"10.3390/ijerph19084461","title":"Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China’s Three Urban Agglomerations as Examples","year":2022,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Jilin Office of Philosophy and Social Science; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; Zhejiang Office of Philosophy and Social Science","keywords":"Urban agglomeration; Air pollution; Beijing; China; Pollution; Air quality index; Environmental planning; Environmental science; Geography; Environmental economics; Business; Meteorology; Economic geography","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":[0.001223784,0.0005555485,0.0003714782,0.003235468,0.0009909412,0.001373915,0.0007021985,0.0006958029,0.0009557792],"category_scores_gemma":[0.004739258,0.0002832894,0.0007787072,0.003550162,0.0009877754,0.002293558,0.001332438,0.0003970144,0.0001199218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515737,"about_ca_system_score_gemma":0.0009498654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0204258,"about_ca_topic_score_gemma":0.02271249,"domain_scores_codex":[0.998949,0.0003666581,0.00005709442,0.0003121437,0.0001819019,0.000133233],"domain_scores_gemma":[0.9983624,0.0007521074,0.0003518024,0.0001162231,0.0003072431,0.0001102248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000330891,0.0002058069,0.370681,0.0006968814,0.0005931805,0.002614397,0.006862157,0.287705,0.004268332,0.1247758,0.005249455,0.1960172],"study_design_scores_gemma":[0.00003236777,0.00009702765,0.09082308,0.0001426078,0.0004213636,0.0005660336,0.004215049,0.83882,0.002370215,0.05291566,0.009511748,0.00008485627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7258599,0.001648687,0.2583492,0.0008335251,0.00005886337,0.0001878458,0.0006377636,0.000155828,0.01226838],"genre_scores_gemma":[0.9797743,0.0005295968,0.0178588,0.00003454412,0.00002380558,0.00007637011,0.0003459501,0.000009130369,0.001347459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0204258,"threshold_uncertainty_score":0.04061383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0522383859227473,"score_gpt":0.3635774430241318,"score_spread":0.3113390571013844,"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."}}