{"id":"W1766904599","doi":"","title":"INSTITUTIONAL EFFECT ANALYSIS COMPARING ENERGY EFFICIENCY RETROFITTING FOR EXISTING RESIDENTIAL BUILDINGS PATTERNS IN CHINA","year":2013,"lang":"en","type":"article","venue":"","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Retrofitting; China; Business; Beijing; Government (linguistics); Incentive; Sustainability; Energy consumption; Central government; Efficient energy use; Local government; Environmental economics; Environmental planning; Economic growth; Engineering; Geography; Economics; Political science; Public administration","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":[0.005713506,0.0004443543,0.0007072267,0.002228004,0.0007665177,0.001171559,0.0008986557,0.0002868371,0.003610446],"category_scores_gemma":[0.009835738,0.0001646399,0.001975754,0.002510894,0.001111515,0.0008000177,0.001442572,0.0005497767,0.0002362574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003189363,"about_ca_system_score_gemma":0.002685732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04560338,"about_ca_topic_score_gemma":0.05501035,"domain_scores_codex":[0.9948111,0.00247919,0.0004097969,0.0006398624,0.0007444209,0.0009156493],"domain_scores_gemma":[0.9869671,0.005967889,0.002582797,0.001304039,0.00209926,0.00107897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004522414,0.000318894,0.9835186,0.00006025576,0.0004918687,0.0001192721,0.0004389586,0.003283108,0.0002788266,0.0006385023,0.0002163987,0.01018305],"study_design_scores_gemma":[0.00002184364,0.0005573849,0.9928533,0.00001446699,0.0002626441,0.00002135533,0.002232758,0.003138849,0.0004108897,0.0001054857,0.000365291,0.00001573261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982937,0.00006260484,0.0003170444,0.00002594846,0.000004066957,0.00003466136,0.0001333722,0.00001155495,0.001117121],"genre_scores_gemma":[0.999217,0.0000347645,0.0001220175,0.000007039281,0.000002445418,0.00002532949,0.0001962465,0.000002384491,0.0003925776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04560338,"threshold_uncertainty_score":0.09067589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009110404943574,"score_gpt":0.2461423742697231,"score_spread":0.2360512702202874,"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."}}