{"id":"W2092556275","doi":"10.1145/1557626.1557659","title":"Use of semantic web technology for adding 3D detail to GIS landscape data","year":2009,"lang":"en","type":"article","venue":"","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Visualization; Ontology; Semantic Web; Geographic information system; Fidelity; Semantics (computer science); Information retrieval; World Wide Web; Data mining; Remote sensing; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005301003,0.00007570973,0.0001217441,0.0001324793,0.00002373629,0.00001207605,0.0003083874,0.00006634721,0.00002450139],"category_scores_gemma":[0.00007339737,0.00007541625,0.00001519233,0.0002363694,0.000007257603,0.00008525614,0.00006014798,0.00005064184,0.00002432018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009055174,"about_ca_system_score_gemma":0.000009458628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060584,"about_ca_topic_score_gemma":0.0001095202,"domain_scores_codex":[0.9994609,0.000001502432,0.000174005,0.0001580317,0.00005227373,0.0001532675],"domain_scores_gemma":[0.9991914,0.00004998757,0.0000153923,0.0006660834,0.00004473154,0.00003238174],"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":[0.0000144158,0.0001146994,0.001697912,0.0001348362,0.0001074805,0.000001203057,0.0001327811,0.6043333,0.1458037,0.0132339,0.06354643,0.1708793],"study_design_scores_gemma":[0.00009300013,0.00003160752,0.0001217734,0.00001788415,0.00002022337,0.000001492559,0.00001097165,0.9764277,0.003745606,0.0003295103,0.01909724,0.0001030367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08216628,0.0000571014,0.9151796,0.0008121675,0.00005087314,0.0003830509,0.0001313526,0.0005536073,0.000665958],"genre_scores_gemma":[0.706809,0.00001204386,0.2929604,0.00004635479,0.0000204638,0.00002456249,0.00004744596,0.00001225844,0.00006747709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6246427,"threshold_uncertainty_score":0.3075385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04666216461603243,"score_gpt":0.2678063126164486,"score_spread":0.2211441480004162,"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."}}