{"id":"W6961252274","doi":"10.14288/1.0300477","title":"Greening Vancouver through Energy Benchmarking : a brief overview of the City’s Benchmarking initiative","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Neuroscience, Education and Cognitive Function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Electricity; Greenhouse gas; Energy consumption; Efficient energy use; Incentive; Consumption (sociology); Process (computing)","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":[0.007310476,0.0007249667,0.000534764,0.005200963,0.004946059,0.009863548,0.001878359,0.001003109,0.003215202],"category_scores_gemma":[0.006463692,0.0004673527,0.0004069081,0.01542382,0.001380935,0.00174954,0.003834738,0.001935589,0.0007510183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0269672,"about_ca_system_score_gemma":0.04381059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.751985,"about_ca_topic_score_gemma":0.8543745,"domain_scores_codex":[0.9919464,0.001872851,0.0003042702,0.0005504042,0.004213332,0.001112773],"domain_scores_gemma":[0.9903874,0.0008533983,0.0002860557,0.0004929329,0.006105336,0.001874911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001078584,0.0004394397,0.0674991,0.001354665,0.00008116771,0.0004915276,0.007279238,0.007828904,0.002110803,0.0502615,0.1500999,0.712446],"study_design_scores_gemma":[0.00001453073,0.0001629213,0.121642,0.001241346,0.00002453412,0.000137695,0.008305945,0.00316045,0.00137519,0.003813151,0.8599783,0.0001438874],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2461139,0.05749129,0.04029852,0.08159826,0.002142217,0.002835238,0.01828995,0.00177021,0.5494604],"genre_scores_gemma":[0.7604049,0.04892112,0.06342546,0.006093294,0.0006164897,0.001646907,0.02208532,0.0008884117,0.09591813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.248015,"threshold_uncertainty_score":0.4989511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05390671703081754,"score_gpt":0.2425809982108767,"score_spread":0.1886742811800591,"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."}}