{"id":"W3035623411","doi":"10.1016/j.jclepro.2020.122430","title":"Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations","year":2020,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Upgrade; Greenhouse gas; Payback period; Occupancy; Energy (signal processing); TOPSIS; Engineering; Efficient energy use; Environmental economics; Computer science; Civil engineering; Operations research; Production (economics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005583182,0.0004478573,0.0003987257,0.0008207029,0.0005903634,0.0006344769,0.0008441347,0.0004191627,0.001979816],"category_scores_gemma":[0.001018178,0.0002971939,0.0006186335,0.001127686,0.0002783105,0.0003813937,0.0002303173,0.0002708856,0.0002505797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007521091,"about_ca_system_score_gemma":0.004561088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9445192,"about_ca_topic_score_gemma":0.9735745,"domain_scores_codex":[0.9997873,0.00002984646,0.000007834254,0.00004334137,0.00005784624,0.00007387028],"domain_scores_gemma":[0.999525,0.0001281945,0.00003046934,0.0000306021,0.0002394506,0.00004624222],"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":[0.0005709627,0.0002514711,0.2001954,0.00006729782,0.0001178202,0.000110321,0.0001409323,0.7746894,0.002012186,0.0007873463,0.004263708,0.01679329],"study_design_scores_gemma":[0.00004350405,0.00005230852,0.2873322,0.00001850589,0.00005906764,0.00001857405,0.0004447671,0.7078869,0.001988751,0.0002621239,0.001828077,0.0000652427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940797,0.00006397409,0.000908667,0.0000543569,0.000004252805,0.00002000709,0.002959378,0.00007628933,0.001833334],"genre_scores_gemma":[0.996218,0.00003272145,0.0007765081,0.000009220343,9.54299e-7,0.000007484271,0.002252877,0.0000154641,0.0006867403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05548084,"threshold_uncertainty_score":0.1116151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02042542014710207,"score_gpt":0.228561862064723,"score_spread":0.2081364419176209,"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."}}