{"id":"W4389039452","doi":"10.1016/j.apenergy.2023.122359","title":"From buildings to cities: How household demographics shape demand response and energy consumption","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University; Canada Excellence Research Chairs, Government of Canada","keywords":"Occupancy; Consumption (sociology); Energy consumption; Demand response; Thermostat; Nexus (standard); Context (archaeology); Renewable energy; Archetype; Economics; Business; Environmental economics; Electricity; Engineering; Geography; Civil engineering; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000213511,0.0003029449,0.0002602502,0.0005191715,0.000108968,0.0001255611,0.0002180484,0.0001566651,0.00003540591],"category_scores_gemma":[0.00001611291,0.0003448851,0.00005017909,0.0006503292,0.00006299199,0.00009105215,0.0001936593,0.00009155367,0.0000196001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006290522,"about_ca_system_score_gemma":0.000007674986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001240992,"about_ca_topic_score_gemma":0.00009306522,"domain_scores_codex":[0.9985851,0.00003294405,0.0002097138,0.0004407254,0.0002649775,0.0004665617],"domain_scores_gemma":[0.9990883,0.0002584419,0.00003136962,0.0003996433,0.00001475149,0.0002074359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003235689,0.00002522622,0.0006714677,0.00006481919,0.0004034277,0.00006356118,0.0005571428,0.7463608,0.07467875,0.06226745,0.09409435,0.02048938],"study_design_scores_gemma":[0.001250186,0.00008006769,0.01747288,0.00007146633,0.0001015009,0.000005173788,0.0004411608,0.08887313,0.02034301,0.002657693,0.8674359,0.00126786],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958843,0.0002442958,0.03703141,0.0003518844,0.0005081101,0.00005434248,0.00003427644,0.001713823,0.00121886],"genre_scores_gemma":[0.9959699,0.001199432,0.001019226,0.0005909991,0.0003104997,0.0002210281,0.00007251136,0.0001194272,0.0004969708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7733415,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157844000918676,"score_gpt":0.1948819519674294,"score_spread":0.1790975518755618,"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."}}