{"id":"W2184885579","doi":"10.26868/25222708.2013.1138","title":"An Optimization Methodology To Evaluate The Effect Size Of Incentives On Energy-cost Optimal Curves","year":2013,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Energy consumption; Mathematical optimization; Life-cycle cost analysis; Energy (signal processing); Incentive; Function (biology); Energy cost; Computer science; Efficient energy use; Reliability engineering; Mathematics; Engineering; Environmental economics; Economics; Statistics; Microeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008022875,0.001053472,0.0008652725,0.002582811,0.0004033839,0.0009732636,0.0007582291,0.0009350396,0.002873096],"category_scores_gemma":[0.02796851,0.0004183141,0.001036004,0.001697019,0.0008949942,0.001424937,0.0008401638,0.001130934,0.0001767788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068435,"about_ca_system_score_gemma":0.001335071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351281,"about_ca_topic_score_gemma":0.0007536972,"domain_scores_codex":[0.9968061,0.0017,0.0001457964,0.0002514042,0.0009390928,0.0001576189],"domain_scores_gemma":[0.9836966,0.01351205,0.001312038,0.0005637336,0.0008118654,0.0001036941],"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":[0.0001501702,0.0002000445,0.002292729,0.000129361,0.0001312228,0.0000312373,0.00004481866,0.9156376,0.00437303,0.02736036,0.0003241332,0.04932538],"study_design_scores_gemma":[0.00007577128,0.0006986488,0.003173881,0.0000382964,0.00004850453,0.00005423568,0.00005054271,0.9737349,0.009605587,0.009944021,0.002532098,0.000043583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07668971,0.0002488792,0.9144457,0.0001602977,0.00003244129,0.000548315,0.0002252628,0.0002233643,0.007426029],"genre_scores_gemma":[0.6191816,0.0001826542,0.3778578,0.00008324563,0.00001435743,0.001270406,0.0001679797,0.00009598059,0.001145913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008022875,"threshold_uncertainty_score":0.04242951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028874291138669,"score_gpt":0.3008246394190818,"score_spread":0.2705358965076951,"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."}}