{"id":"W3208783625","doi":"10.32920/ryerson.14663082.v1","title":"Estimating power consumption in City of Toronto: a case study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Electricity demand; Electricity generation; Renewable energy; Environmental economics; Consumption (sociology); Electricity retailing; Peak demand; Energy demand; Business; Economics; Electricity market; Power (physics); Engineering; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002359086,0.0001943792,0.0003407837,0.00003601673,0.0000168314,0.00003530012,0.00008936764,0.0001492958,0.0006469409],"category_scores_gemma":[0.00003985257,0.0002072603,0.0000638309,0.0000370765,0.0000112285,0.00008247566,0.0002176642,0.0003199791,9.049514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296481,"about_ca_system_score_gemma":0.0000221738,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01540603,"about_ca_topic_score_gemma":0.0400648,"domain_scores_codex":[0.9990223,0.00003964047,0.0004325049,0.0002239448,0.0001191888,0.0001623908],"domain_scores_gemma":[0.9994997,0.00006593978,0.00006002405,0.0003005406,0.00003380131,0.00003999793],"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":[0.000005420569,0.0003042882,0.1618281,0.0009806929,0.0002084637,0.003855144,0.01942676,0.805849,0.0003832573,0.00004251168,0.00003195112,0.007084437],"study_design_scores_gemma":[0.001080287,0.0000955078,0.02115999,0.001631049,0.00008971164,0.0008674811,0.01180939,0.9610507,0.001173912,0.00003800632,0.00001881513,0.0009851091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985913,0.000832365,0.00382581,8.484392e-7,0.0009860966,0.000184067,0.000005306189,0.0001456373,0.008106888],"genre_scores_gemma":[0.9889413,0.00001517609,0.01091757,0.000002878884,0.00003489233,0.00002399445,0.00001123579,0.00002498578,0.00002803944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1552017,"threshold_uncertainty_score":0.9911504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022307324850706,"score_gpt":0.2822693014475525,"score_spread":0.2520462281990454,"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."}}