{"id":"W3215516035","doi":"10.1016/j.enbuild.2021.111706","title":"Characterizing and structuring urban GIS data for housing stock energy modelling and retrofitting","year":2021,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Horizon 2020; Horizon 2020 Framework Programme; Natural Resources Canada; Canada Excellence Research Chairs, Government of Canada; HORIZON EUROPE Framework Programme; Bundesministerium für Bildung und Forschung","keywords":"CityGML; 3D city models; Computer science; Workflow; Geographic information system; Spatial analysis; Rendering (computer graphics); Geospatial analysis; Built environment; Database; Data science; Data mining; Visualization; Civil engineering; Engineering; Cartography; Geography; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001140896,0.0001707435,0.0001939843,0.0000576707,0.0002657985,0.0001421751,0.0001120528,0.00009552412,0.000002201061],"category_scores_gemma":[0.00002244633,0.0001949571,0.00001720934,0.00008847075,0.00003336005,0.0002826514,0.0002272323,0.00008696676,3.390107e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001792927,"about_ca_system_score_gemma":0.00001207638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001482162,"about_ca_topic_score_gemma":0.00003843051,"domain_scores_codex":[0.9990228,0.000009102152,0.000214543,0.0004255233,0.0000766859,0.0002513016],"domain_scores_gemma":[0.9994342,0.00008910282,0.00004562406,0.0003042934,0.00003969112,0.00008704409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003594006,0.00003102038,0.001985538,0.0007359153,0.0003306211,0.00001757851,0.001569301,0.3127096,0.3378468,0.06241075,0.0005307858,0.2817962],"study_design_scores_gemma":[0.0002062149,0.000009070178,0.00006869056,0.00009591777,0.00003646299,0.00002191359,0.00004471833,0.9620984,0.02037852,0.001489393,0.01530089,0.0002498422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4613127,0.001884029,0.5364339,0.00007192928,0.00007521645,0.00002839094,0.00003043881,0.0001069603,0.00005636295],"genre_scores_gemma":[0.9139125,0.0009290109,0.0846118,0.00008594866,0.0002532408,0.00001638023,0.0001037288,0.00004901344,0.00003835994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6493888,"threshold_uncertainty_score":0.7950119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02363714195444721,"score_gpt":0.2218429664337364,"score_spread":0.1982058244792892,"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."}}