{"id":"W2128808278","doi":"10.1109/comgeo.2013.7","title":"Generating Bridge Structure Model Details by Fusing GIS Source Data Using Semantic Web Technology","year":2013,"lang":"en","type":"article","venue":"","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Data mining; Bridge (graph theory); Data modeling; Data model (GIS); Fidelity; Terrain; Information retrieval; Database; Artificial intelligence","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":[0.000945288,0.001042007,0.0007190096,0.004263092,0.0004187647,0.001602779,0.0009607039,0.001121601,0.001745509],"category_scores_gemma":[0.002724852,0.001070976,0.002055845,0.003012352,0.0006609657,0.002532262,0.00224246,0.0008645254,0.0007533794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005001623,"about_ca_system_score_gemma":0.0009662009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00368503,"about_ca_topic_score_gemma":0.008994261,"domain_scores_codex":[0.9993981,0.00007240315,0.00005095864,0.0001069067,0.0003512758,0.00002030702],"domain_scores_gemma":[0.999027,0.0003468115,0.00009725425,0.0003475994,0.0001559245,0.0000254421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001377491,0.0002420596,0.009033547,0.0005076955,0.0002913829,0.0009764924,0.001065338,0.5048953,0.03484603,0.02504938,0.004829401,0.4181256],"study_design_scores_gemma":[0.00002320714,0.00005365255,0.003066455,0.00006757888,0.00007426547,0.0003581413,0.0004122122,0.9246052,0.02785902,0.02279461,0.02060631,0.00007944078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01572082,0.00006888896,0.9777027,0.00006687264,0.0000153258,0.00008586544,0.001003559,0.003952283,0.001383703],"genre_scores_gemma":[0.164213,0.0003177336,0.8289227,0.00004494357,0.00001227694,0.0001865385,0.004942351,0.0006408509,0.0007196085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004263092,"threshold_uncertainty_score":0.007327139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02702800064161101,"score_gpt":0.2429305161604473,"score_spread":0.2159025155188363,"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."}}