{"id":"W4389331807","doi":"10.5194/isprs-annals-x-1-w1-2023-97-2023","title":"ENABLING INTEROPERABILITY OF URBAN BUILDING ENERGY DATA BASED ON OGC API STANDARDS AND CITYGML 3D CITY MODELS","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Resources Canada; Bundesministerium für Bildung und Forschung","keywords":"CityGML; Web Coverage Service; Interoperability; Computer science; Geospatial analysis; Application programming interface; Database; Sensor web; Testbed; World Wide Web; Web service; Visualization; Operating system; Geography; Data mining; Web mapping; Web standards; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00659504,0.0009791142,0.0007999578,0.00431613,0.001338648,0.009006339,0.002794467,0.001277654,0.003028244],"category_scores_gemma":[0.01382659,0.0007379988,0.001522776,0.007342132,0.001432854,0.006000882,0.006018533,0.002356268,0.002197057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003345717,"about_ca_system_score_gemma":0.005524521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0511018,"about_ca_topic_score_gemma":0.04713199,"domain_scores_codex":[0.9946492,0.001088736,0.0006613565,0.0005431374,0.002640421,0.0004170384],"domain_scores_gemma":[0.9889902,0.00154799,0.0004865506,0.005848785,0.00282169,0.0003047082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008751578,0.0007542101,0.03638602,0.0008171989,0.000452575,0.001169597,0.004814387,0.1882195,0.01894329,0.3760636,0.07820185,0.2933026],"study_design_scores_gemma":[0.0001205165,0.00008574224,0.01226938,0.0005244751,0.0001364482,0.0002539323,0.001922533,0.4677018,0.04502543,0.08730955,0.384345,0.0003052375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05597698,0.0002164464,0.8234732,0.001621649,0.0002856951,0.0008712307,0.02231618,0.04437695,0.05086169],"genre_scores_gemma":[0.4259133,0.0005281665,0.4781712,0.0006602129,0.00006433028,0.001176956,0.07759997,0.008055425,0.00783043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0511018,"threshold_uncertainty_score":0.1016087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08418290754031164,"score_gpt":0.3159703051163066,"score_spread":0.2317873975759949,"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."}}