{"id":"W2910848777","doi":"10.1016/j.energy.2019.01.067","title":"Analysis of regional difference decomposition of changes in energy consumption in China during 1995–2015","year":2019,"lang":"en","type":"article","venue":"Energy","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Energy consumption; Energy intensity; China; Consumption (sociology); Energy (signal processing); Intensity (physics); Efficient energy use; Decomposition; Economics; Energy conservation; Environmental science; Agricultural economics; Natural resource economics; Geography; Statistics; Ecology; Mathematics; Physics; Biology","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":[0.000747926,0.0003739599,0.000351783,0.002359837,0.0003173759,0.00062076,0.0005143569,0.0003274909,0.0009980591],"category_scores_gemma":[0.0006980365,0.0002502011,0.001102038,0.003663369,0.0002702145,0.0003798874,0.0006858243,0.0002303068,0.000178923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002613546,"about_ca_system_score_gemma":0.001612964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2157116,"about_ca_topic_score_gemma":0.2413232,"domain_scores_codex":[0.999691,0.00002668695,0.00003939771,0.00009189098,0.00005790977,0.00009310202],"domain_scores_gemma":[0.9995399,0.0000513465,0.0001379319,0.00003991044,0.0001516481,0.00007925944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000184876,0.00004353035,0.9857894,0.0000731543,0.0004339367,0.0002009798,0.0004671593,0.002768632,0.001713803,0.0004335442,0.0007380844,0.007152975],"study_design_scores_gemma":[0.000001171292,0.000008594694,0.9983651,0.000002835527,0.00002843099,0.00001265372,0.0001481722,0.0009870174,0.00009468036,0.00001216337,0.0003357741,0.000003436305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975374,0.0001770233,0.0001216801,0.00004044978,0.000004516044,0.000005248544,0.001743164,0.000008487908,0.0003621362],"genre_scores_gemma":[0.9960923,0.0001071697,0.0001213565,0.00001189528,0.000004479408,0.000007529341,0.003112533,0.000002292247,0.0005404842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2157116,"threshold_uncertainty_score":0.428912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006837684347103779,"score_gpt":0.2394498331078665,"score_spread":0.2326121487607627,"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."}}