{"id":"W3211200678","doi":"10.32920/ryerson.14654385.v1","title":"Deep energy retrofits: Toronto's urban single family housing stock","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Upgrade; Capital cost; Energy consumption; Process (computing); Stock (firearms); Architectural engineering; Demolition; Engineering; Civil engineering; Computer science; Mechanical engineering; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000575198,0.0003577379,0.0003515622,0.00005861737,0.00006911512,0.0001995494,0.0002654191,0.0005174436,0.000291371],"category_scores_gemma":[0.000008859442,0.0004018143,0.0001482363,0.0001058734,0.00001638219,0.0001910783,0.0002842176,0.0002925882,8.223661e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004901493,"about_ca_system_score_gemma":0.00004520932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001335092,"about_ca_topic_score_gemma":0.002135788,"domain_scores_codex":[0.9987038,0.00002874883,0.0003246666,0.000403247,0.00021791,0.0003215803],"domain_scores_gemma":[0.9991621,0.00002073903,0.00005881098,0.0005902464,0.00007795942,0.00009008333],"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":[0.00000283467,0.00002529728,0.00003517774,0.00006133108,0.0001027723,0.000008143983,0.0001450671,0.9861714,0.0007231338,0.001362532,0.001652211,0.009710163],"study_design_scores_gemma":[0.0001633581,0.00002253708,0.0001127646,0.0001903574,0.00005648682,0.000003850008,0.0002293301,0.9883412,0.003823749,0.0001145241,0.006222919,0.0007189483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03091215,0.009858352,0.8775562,0.00001504678,0.002774464,0.00007840819,0.000003843511,0.00178037,0.07702114],"genre_scores_gemma":[0.973617,0.0007709938,0.02358903,0.0001842765,0.0004267346,0.00003923668,0.0002936646,0.0001383525,0.0009407422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9427048,"threshold_uncertainty_score":0.9998434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247589269299633,"score_gpt":0.1929599433726263,"score_spread":0.1804840506796299,"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."}}