{"id":"W3176005648","doi":"10.32920/ryerson.14644146.v1","title":"A comparison of bottom-up methods for estimating institutional building energy use to inform resource and emission reduction strategies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Top-down and bottom-up design; Christian ministry; Reduction (mathematics); Environmental economics; Resource (disambiguation); Scale (ratio); Demand reduction; Energy consumption; Environmental science; Efficient energy use; Energy (signal processing); Uncertainty reduction theory; Computer science; Environmental resource management; Engineering; Statistics; Economics; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004443741,0.0002626905,0.0004620825,0.0002553572,0.0001450849,0.0003412282,0.000128647,0.0002343836,0.00000850693],"category_scores_gemma":[0.0002055865,0.0002693602,0.00008495816,0.0001963256,0.00003333391,0.0002815961,0.0002752134,0.0002856121,3.690553e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000240018,"about_ca_system_score_gemma":0.0002779324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000146544,"about_ca_topic_score_gemma":0.000002128836,"domain_scores_codex":[0.9986982,0.00004633017,0.000530487,0.0003112593,0.0001721787,0.0002415343],"domain_scores_gemma":[0.9991409,0.0002174277,0.0001178726,0.0002375848,0.000176349,0.0001099127],"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":[0.00002241506,0.00001860365,0.00002608983,0.00112178,0.000081912,7.393018e-7,0.0009246977,0.8475757,0.03989033,0.01126042,0.0006886852,0.09838862],"study_design_scores_gemma":[0.0002010164,0.00004238544,0.00003935877,0.000731482,0.00005225126,0.000009620489,0.005187256,0.9056276,0.07919186,0.001434585,0.007159574,0.0003230225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09659247,0.0003983995,0.9014925,0.00004080931,0.0006101409,0.0002823555,0.000004499995,0.0001823011,0.0003965416],"genre_scores_gemma":[0.3473809,0.00001159281,0.6522142,0.000007165876,0.00008078762,0.00008502207,0.00004828608,0.00002161138,0.000150418],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2507885,"threshold_uncertainty_score":0.9999759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05761938472645824,"score_gpt":0.3953219838546628,"score_spread":0.3377025991282046,"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."}}