{"id":"W4417101070","doi":"10.5539/ijsp.v12n5p22","title":"China&amp;#39;s Provincial Digital Economy and Carbon Emissions: A Spatio-Temporal Analysis During 2013–2019","year":2023,"lang":"","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Per capita; Digital economy; Context (archaeology); Index (typography); Low-carbon economy; TOPSIS; Urbanization; Green economy; Spatial analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001263111,0.0003485129,0.000897342,0.0009089059,0.0001513284,0.0005969285,0.0004154334,0.0001757551,0.0003544399],"category_scores_gemma":[0.0005629266,0.0004027959,0.000235429,0.0002867496,0.000306571,0.0006769381,0.0003901346,0.0003834303,0.00002368908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004874179,"about_ca_system_score_gemma":0.0001335419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006375012,"about_ca_topic_score_gemma":0.0003264794,"domain_scores_codex":[0.9965684,0.00004993724,0.002184052,0.0006662309,0.0001631543,0.0003682506],"domain_scores_gemma":[0.9968383,0.0001986992,0.002145511,0.0003016093,0.0001750129,0.0003409144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003130454,0.0002969761,0.9675183,0.0001427091,0.00245537,0.00005117471,0.0008250971,0.003946151,0.000009639677,0.01988562,0.0004548636,0.004101062],"study_design_scores_gemma":[0.001479754,0.0001707197,0.8063315,0.00005572573,0.0001949062,0.00005136891,0.00005171521,0.02242006,0.000009744835,0.1594518,0.009297919,0.0004847489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865762,0.000996612,0.004333007,0.001687551,0.0008396591,0.0002505315,0.004367044,0.00001187494,0.0009375376],"genre_scores_gemma":[0.9923838,0.003096746,0.002978785,0.00002929749,0.0003867074,0.000008848159,0.0002501715,0.00003198489,0.0008336772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1611868,"threshold_uncertainty_score":0.9998424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525357130836212,"score_gpt":0.2277245060608288,"score_spread":0.2124709347524667,"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."}}