{"id":"W4411161940","doi":"10.1016/j.apgeog.2025.103689","title":"Decoding the dynamics and disparities of urban carbon intensity under the influence of land use and demographics from both global and local perspectives","year":2025,"lang":"en","type":"article","venue":"Applied Geography","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council; National Natural Science Foundation of China; Key Laboratory of Coastal Zone Development and Protection; Ministry of Natural Resources","keywords":"Demographics; Geography; Dynamics (music); Land use; Economic geography; Regional science; Demography; Ecology; Sociology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002264566,0.00009602011,0.0001563285,0.00002710422,0.0001785106,0.00002909101,0.00009285123,0.00006117387,0.000001381589],"category_scores_gemma":[0.00001951034,0.00006153461,0.00002217853,0.0002706973,0.002215002,0.0000675128,0.0001930392,0.0001211036,4.207757e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001926949,"about_ca_system_score_gemma":0.000008520432,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01242741,"about_ca_topic_score_gemma":0.006107191,"domain_scores_codex":[0.9993602,0.00003903793,0.00014994,0.0001818344,0.0001227625,0.0001461937],"domain_scores_gemma":[0.9994041,0.0002965059,0.00007503141,0.0001617786,0.00001114454,0.00005142064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005295143,0.00001603551,0.9711859,0.00002097297,0.0000317539,1.117726e-7,0.0007767214,0.0001505238,0.00001706634,0.02693044,0.00001704911,0.0008004908],"study_design_scores_gemma":[0.0001732664,0.00002329714,0.9795448,0.00002264533,0.00004230621,7.302456e-7,0.004649964,0.002058618,0.00001144551,0.01340147,0.00001201331,0.00005943674],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968674,0.0008005488,0.000856161,0.001019416,0.00001094077,0.0001659132,0.00003501067,0.000007990548,0.0002365727],"genre_scores_gemma":[0.9983772,0.0006337193,0.0001849996,0.0007916553,0.00000350072,0.000003641265,0.000001484282,0.000002399246,0.000001421309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01352897,"threshold_uncertainty_score":0.9941489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009624618852888258,"score_gpt":0.2383242864436419,"score_spread":0.2286996675907536,"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."}}