{"id":"W77517711","doi":"10.1007/978-3-642-29793-9_15","title":"A Three Step Procedure to Enrich Augmented Reality Games with CityGML 3D Semantic Modeling","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"CityGML; Computer science; Augmented reality; 3D city models; Data model (GIS); Extension (predicate logic); Domain (mathematical analysis); Data modeling; Component (thermodynamics); Metamodeling; Geospatial analysis; Information model; Data mining; Software engineering; Programming language; Human–computer interaction; Artificial intelligence; Visualization; Geography; Cartography; Mathematics","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":[0.0008541983,0.001597513,0.0005735354,0.00133418,0.001013971,0.003008117,0.001836769,0.001034064,0.02863397],"category_scores_gemma":[0.002358794,0.0008687344,0.001749583,0.0008792924,0.0008294773,0.00209389,0.004979171,0.001787137,0.01169951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005465374,"about_ca_system_score_gemma":0.001213698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003731608,"about_ca_topic_score_gemma":0.007727604,"domain_scores_codex":[0.9990827,0.0001591414,0.00004978583,0.0001483135,0.0004838168,0.00007623936],"domain_scores_gemma":[0.9993377,0.0001469536,0.00001805337,0.0002426849,0.0002011816,0.0000532476],"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":[0.0004247213,0.0007101235,0.001025822,0.0006214865,0.0001486824,0.0009569001,0.004221556,0.03310801,0.09993286,0.2793225,0.04192841,0.5375988],"study_design_scores_gemma":[0.00009670628,0.0002089055,0.0008576085,0.0001724663,0.0001345445,0.0009004432,0.001165843,0.287847,0.1503743,0.09501426,0.4629976,0.00023024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004151913,0.0000263595,0.9660777,0.00009982738,0.00007416037,0.0003660463,0.0005676831,0.008207661,0.02042866],"genre_scores_gemma":[0.04419023,0.00006405562,0.9265723,0.00008598403,0.000007414786,0.0003892634,0.001278519,0.001972423,0.02543977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02863397,"threshold_uncertainty_score":0.09579021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102855547510358,"score_gpt":0.206100956164808,"score_spread":0.1950724006897044,"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."}}