{"id":"W4405509285","doi":"10.1007/978-981-97-8309-0_21","title":"A Surrogate Urban Building Energy Model for Predicting Cooling Energy Consumption in a Hot and Arid Climate","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Energy consumption; Arid; Environmental science; Energy (signal processing); Climate model; Desert climate; Consumption (sociology); Climatology; Climate change; Engineering; Geology; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001877369,0.0005666525,0.0005218069,0.0007417032,0.00004799495,0.00009008688,0.0001371049,0.0006397911,0.00000818925],"category_scores_gemma":[0.00003970208,0.0006709903,0.0001082322,0.0001200976,0.00002153602,0.0001338057,0.00008268654,0.0005971315,2.481237e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002637219,"about_ca_system_score_gemma":0.00001992127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000287895,"about_ca_topic_score_gemma":0.001185489,"domain_scores_codex":[0.9982753,0.000004700307,0.0005213516,0.000510154,0.0001537294,0.0005348052],"domain_scores_gemma":[0.9993452,0.0002704676,0.00006607776,0.0002180985,0.00002388605,0.00007628005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002883526,0.000003473326,0.00003035005,0.0007654468,0.00006293627,0.00001616123,0.0001541814,0.9613678,0.0004965903,0.03487179,0.000007049006,0.002195312],"study_design_scores_gemma":[0.0003358415,0.00001641891,0.000003647973,0.002062207,0.00005770772,0.00001876561,5.800698e-7,0.9902508,0.0005803711,0.00495578,0.001124167,0.0005937774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001417842,0.01049426,0.9853663,0.00001035212,0.0006777925,0.0001578226,0.00005935348,0.0006813179,0.001134995],"genre_scores_gemma":[0.9873591,0.003278857,0.008021232,0.00003608145,0.0003242135,0.0001329945,0.000133131,0.0003493303,0.0003650441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9859413,"threshold_uncertainty_score":0.9995741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009736843242255147,"score_gpt":0.1979499204271224,"score_spread":0.1882130771848673,"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."}}