{"id":"W2484355819","doi":"10.4018/978-1-4666-0327-1.ch003","title":"Toward an Architecture for Enhancing Semantic Interoperability Based on Enrichment of Geospatial Data Semantics","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in geospatial technologies book series","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Semantic interoperability; Computer science; Geospatial analysis; Interoperability; Ontology; Semantic grid; Semantic Web Stack; Semantics (computer science); Semantic computing; Information retrieval; Semantic Web; Semantic integration; Semantic technology; World Wide Web; Semantic analytics; Geography; Programming language","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":[0.002720518,0.0008755687,0.0006190316,0.001950865,0.001478174,0.005903422,0.002427101,0.002083933,0.004100161],"category_scores_gemma":[0.002129533,0.0008239418,0.001496118,0.003662429,0.002198982,0.01135819,0.005423223,0.003877214,0.002586032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002469212,"about_ca_system_score_gemma":0.00315975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002991392,"about_ca_topic_score_gemma":0.003264044,"domain_scores_codex":[0.998817,0.0002226311,0.00009825047,0.0002074651,0.0005586109,0.00009605636],"domain_scores_gemma":[0.99927,0.000160999,0.00004272497,0.0002090136,0.0002349141,0.00008230845],"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":[0.00001558672,0.0000757576,0.0002508267,0.000198341,0.00003368254,0.0001634759,0.001022041,0.007881507,0.00461244,0.8890729,0.01021237,0.08646113],"study_design_scores_gemma":[0.00001709144,0.00003387365,0.0002807937,0.0002346307,0.00007535559,0.0004360334,0.0004166467,0.08224518,0.007359953,0.4990173,0.4098414,0.00004184773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003921911,0.001361724,0.9512862,0.002351221,0.0002284318,0.0001546928,0.00006791225,0.00181307,0.03881495],"genre_scores_gemma":[0.04487626,0.003754525,0.9122828,0.0007989131,0.000119346,0.0002997525,0.0007718762,0.0005987956,0.03649771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005903422,"threshold_uncertainty_score":0.01791549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520092816590592,"score_gpt":0.273368168398109,"score_spread":0.2481672402322031,"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."}}