{"id":"W4417101070","doi":"10.5539/ijsp.v12n5p22","title":"China'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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004704038,0.0001805954,0.0002330783,0.00170427,0.0006114303,0.0008079073,0.0003702401,0.0002340468,0.001510394],"category_scores_gemma":[0.0008798544,0.000148204,0.0006589059,0.004979304,0.0003608581,0.0004498402,0.0005614258,0.0002571322,0.0001994129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003658044,"about_ca_system_score_gemma":0.003903054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4517046,"about_ca_topic_score_gemma":0.5191026,"domain_scores_codex":[0.9997066,0.00002471767,0.00002735812,0.00004928446,0.0001017796,0.00009016242],"domain_scores_gemma":[0.9994717,0.00006348763,0.0001266684,0.00003217087,0.0002269232,0.00007903078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009916566,0.00004435145,0.9737534,0.0001379909,0.0001459684,0.0005883529,0.001061792,0.003402754,0.001029421,0.001816408,0.003221674,0.01469872],"study_design_scores_gemma":[0.000002126173,0.0000152146,0.9909821,0.00001924924,0.00003274481,0.00007100867,0.001317482,0.003698718,0.0002639304,0.00009459073,0.003490902,0.00001199936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989599,0.0003534419,0.0003880489,0.0003436579,0.00001353266,0.00001661498,0.005548146,0.00002711353,0.00371032],"genre_scores_gemma":[0.9942002,0.0003267236,0.0002554039,0.00002448752,0.000007295338,0.00001389035,0.003662431,0.000004994718,0.001504512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4517046,"threshold_uncertainty_score":0.8981507,"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."}}