{"id":"W4406021882","doi":"10.1080/17538947.2024.2448572","title":"Carbon balance dynamic evolution and simulation coupling economic development and ecological protection: a case study of Jiangxi Province at county scale from 2000–2030","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of Toronto","funders":"","keywords":"Sustainable development; Balance (ability); Climate change; Scale (ratio); Carbon sequestration; Zoning; Carbon fibers; Natural resource economics; Environmental resource management; Environmental science; Geography; Ecology; Economics; Carbon dioxide; Political science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058627,0.0001095418,0.0002602665,0.0001973003,0.00005072623,0.00008648526,0.0001082757,0.00005841866,0.00001641245],"category_scores_gemma":[0.00004051483,0.0001221752,0.00003515611,0.00003283877,0.00005559302,0.0003860318,0.0001065369,0.00009926478,0.000004119376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007046803,"about_ca_system_score_gemma":0.00006660834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001895579,"about_ca_topic_score_gemma":0.0005482655,"domain_scores_codex":[0.9988732,0.000007364617,0.0007317942,0.0002399701,0.00005301119,0.00009462021],"domain_scores_gemma":[0.9991055,0.0000674388,0.0006607716,0.00008305875,0.00003783099,0.00004542454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001707513,0.0003858457,0.8889553,0.00001436731,0.0003112684,0.0000914021,0.0004238089,0.1065951,0.00005578154,0.0005629302,0.0000029911,0.002430455],"study_design_scores_gemma":[0.002662724,0.000307376,0.6245227,0.00006342898,0.00001809278,0.0002119321,0.0006176861,0.3674906,0.000119666,0.002915571,0.0008342023,0.0002359931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970296,0.0004088684,0.001380615,0.00008077152,0.0002971212,0.0001874346,0.00008164156,0.000005697569,0.0005282458],"genre_scores_gemma":[0.9995062,0.00002682877,0.0002053504,0.00001210849,0.00004342854,0.000005942702,0.000005415998,0.000007445446,0.0001872334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2644325,"threshold_uncertainty_score":0.498216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304918216305197,"score_gpt":0.2161547201906908,"score_spread":0.2031055380276388,"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."}}