{"id":"W3132304008","doi":"10.21203/rs.3.rs-160873/v1","title":"Energy consumption and economic development in Guangdong, China: Distribution, relationship and causes","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Sun Yat-sen University","keywords":"Per capita; Energy consumption; Energy intensity; Economics; Granger causality; Consumption (sociology); Vector autoregression; Econometrics; China; Real gross domestic product; Sustainable development; Panel data; Distribution (mathematics); Geography; Mathematics","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":[0.0003567204,0.0001858205,0.0001831461,0.00139766,0.0002455752,0.0004588049,0.0001585248,0.0001171801,0.001181255],"category_scores_gemma":[0.000605509,0.0001265628,0.0003122265,0.003156343,0.0004016646,0.0002709626,0.0005288477,0.0001849367,0.00008281844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009295222,"about_ca_system_score_gemma":0.0005585044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04778207,"about_ca_topic_score_gemma":0.04701842,"domain_scores_codex":[0.9998541,0.000026952,0.00001082594,0.00004831677,0.00003120398,0.00002860597],"domain_scores_gemma":[0.9993642,0.0001751775,0.0002417185,0.00005815157,0.00009311135,0.00006765073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002526858,0.000008217678,0.9943237,0.00001904606,0.00006821312,0.0001178263,0.000200321,0.001161465,0.0003334032,0.0004738471,0.0002013783,0.003067289],"study_design_scores_gemma":[0.000001443311,0.000005507969,0.9983224,0.000003228536,0.00001528597,0.00001435719,0.0001431679,0.001010973,0.00006152544,0.0001232994,0.0002966081,0.000002143109],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985614,0.0001732327,0.0001376857,0.0001014025,0.000001726217,0.000002135711,0.0004430448,0.000003538988,0.0005756775],"genre_scores_gemma":[0.9992655,0.0001038635,0.00005194013,0.000003745899,0.000002599479,0.00000207036,0.0003409282,9.050064e-7,0.0002285246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04778207,"threshold_uncertainty_score":0.0950079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07711753432004642,"score_gpt":0.3004646730936846,"score_spread":0.2233471387736382,"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."}}