{"id":"W3041053309","doi":"10.3390/ijgi9090502","title":"Tools for BIM-GIS Integration (IFC Georeferencing and Conversions): Results from the GeoBIM Benchmark 2019","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"H2020 Marie Skłodowska-Curie Actions; H2020 European Research Council","keywords":"CityGML; Benchmark (surveying); Georeference; Interoperability; Computer science; Context (archaeology); Building information modeling; Data integration; 3D city models; Systems engineering; Data conversion; Database; Software engineering; Data mining; Data science; Visualization; Engineering; World Wide Web; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.02557602,0.002912304,0.001421022,0.008919491,0.001507879,0.008522718,0.0038906,0.002574159,0.004792121],"category_scores_gemma":[0.03303573,0.0007653058,0.001822697,0.01022872,0.001882982,0.007085729,0.006983179,0.00285476,0.004580167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004644462,"about_ca_system_score_gemma":0.006057898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02496912,"about_ca_topic_score_gemma":0.009497759,"domain_scores_codex":[0.9737321,0.005453807,0.001564962,0.002223159,0.01484128,0.002184799],"domain_scores_gemma":[0.978446,0.003859915,0.0008866864,0.004451472,0.01076339,0.001592451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002171889,0.002878977,0.02572268,0.004140267,0.0003954819,0.0006926322,0.003590744,0.05460114,0.017111,0.08747637,0.1652753,0.6359435],"study_design_scores_gemma":[0.000460236,0.001195503,0.05704863,0.003629342,0.0004734595,0.0007812693,0.005367151,0.08749954,0.05104901,0.0249399,0.7670777,0.0004782713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3285998,0.01615034,0.2953797,0.009459246,0.0020442,0.003104891,0.09009954,0.04860998,0.2065523],"genre_scores_gemma":[0.3306428,0.006042253,0.3427523,0.001031921,0.0002816683,0.001544128,0.2958155,0.009048681,0.0128408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02557602,"threshold_uncertainty_score":0.1352606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735441755826316,"score_gpt":0.2249346726860041,"score_spread":0.2075802551277409,"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."}}