{"id":"W2981987727","doi":"10.1088/1757-899x/609/7/072039","title":"Towards a universal ranking system for design parameters’ impact on buildings’ lifecycle energy","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Ranking (information retrieval); Sensitivity (control systems); Energy consumption; Scope (computer science); Energy (signal processing); Computer science; Window (computing); Environmental science; Orientation (vector space); Simulation; Mathematics; Statistics; Engineering; Artificial intelligence; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007732461,0.001957717,0.001440683,0.00962234,0.0005945425,0.003853634,0.0009376603,0.0008629666,0.005130491],"category_scores_gemma":[0.02226323,0.0004351145,0.001269268,0.004366986,0.0007390762,0.00217417,0.001745549,0.0009865429,0.001779088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001457698,"about_ca_system_score_gemma":0.0010995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004916721,"about_ca_topic_score_gemma":0.003235165,"domain_scores_codex":[0.9930477,0.002870083,0.0006061457,0.000661984,0.00240864,0.0004054956],"domain_scores_gemma":[0.9893137,0.00494193,0.0008398754,0.00137417,0.003331921,0.0001985027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004553747,0.0006077136,0.04959003,0.001025011,0.0003715155,0.0002142599,0.0005065766,0.4067874,0.01473722,0.02969919,0.008182269,0.4878235],"study_design_scores_gemma":[0.0000238532,0.0004683487,0.02331645,0.0002225714,0.0001265764,0.0001631459,0.000478049,0.9439163,0.006931628,0.01561142,0.008628981,0.0001125907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1663206,0.0008467306,0.8059849,0.0003171055,0.00008517824,0.0004610367,0.002893046,0.005033699,0.01805771],"genre_scores_gemma":[0.6913791,0.0002984206,0.3034989,0.00006263776,0.00003754788,0.0003002989,0.00273426,0.000305504,0.001383305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00962234,"threshold_uncertainty_score":0.04089367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123585713553238,"score_gpt":0.197234027494986,"score_spread":0.1848754561396622,"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."}}