{"id":"W3210411330","doi":"10.32920/ryerson.14668131.v1","title":"Advanced Cluster and Predictive Analysis Tool Development for Commercial Office Real Estate Energy Usage","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy and Environmental Systems","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Computer science; Object (grammar); Engineering; Business; Artificial intelligence; Marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.0005920587,0.0002195385,0.0004417501,0.00008730646,0.0005108925,0.0001667735,0.0002058003,0.000304956,0.0001683663],"category_scores_gemma":[0.00003726603,0.0002141953,0.0001686183,0.000180517,0.0001634601,0.0001642714,0.0004437965,0.0001340973,0.000001462008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002997899,"about_ca_system_score_gemma":0.0001914078,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01575542,"about_ca_topic_score_gemma":0.09934462,"domain_scores_codex":[0.9981242,0.0002657694,0.0003682255,0.0005688096,0.0003505143,0.0003224847],"domain_scores_gemma":[0.999225,0.000210466,0.0001745084,0.0002133292,0.00004696759,0.0001297137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008411687,0.0005701665,0.04826023,0.0003931035,0.007970915,0.00003385728,0.1705654,0.1227465,0.000188183,0.004817172,0.001559027,0.6420543],"study_design_scores_gemma":[0.002507142,0.0001596664,0.3152229,0.0002749859,0.002324414,0.000001623476,0.06724341,0.00879713,0.001397952,0.0003716026,0.5989171,0.002782136],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8201509,0.00009446359,0.07353033,0.0002876978,0.000702829,0.0005711931,0.00007103046,0.00008626605,0.1045053],"genre_scores_gemma":[0.9801356,0.002144486,0.006651937,0.000187108,0.0002069375,0.0003404414,0.0005987628,0.0000186301,0.009716084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6392722,"threshold_uncertainty_score":0.9907988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567790261285227,"score_gpt":0.2744979765327588,"score_spread":0.2588200739199065,"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."}}