{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000122176,0.0003226828,0.0001607882,0.0004441257,0.0005925035,0.000769282,0.0004731665,0.0001579856,0.002746401],"category_scores_gemma":[0.0003531552,0.000122565,0.0002480425,0.001086857,0.0002492986,0.0002445952,0.0002984364,0.0001525728,0.0001634376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009146599,"about_ca_system_score_gemma":0.003000613,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7939494,"about_ca_topic_score_gemma":0.9475712,"domain_scores_codex":[0.9998693,0.00000769123,0.000003235682,0.00001339448,0.00007736898,0.0000290305],"domain_scores_gemma":[0.9998844,0.0000110848,0.00001685101,0.00001138091,0.00005191463,0.00002435597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008881487,0.00023034,0.5014231,0.0003291988,0.0001951946,0.002080564,0.003661199,0.2191805,0.02356064,0.007654747,0.0120752,0.2287211],"study_design_scores_gemma":[0.00002644165,0.0003620502,0.8924111,0.00004237971,0.00008952308,0.0002105849,0.007037,0.072093,0.008154267,0.0005740489,0.0189584,0.0000412154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929342,0.0001222403,0.0007318794,0.0000629487,0.000004370068,0.00002254747,0.001037376,0.00002986539,0.00505468],"genre_scores_gemma":[0.9951814,0.000103211,0.00094376,0.000004984982,0.000001250616,0.000006307073,0.0006098401,0.000008347,0.003140785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2060506,"threshold_uncertainty_score":0.4145282,"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."}}