{"id":"W2121773598","doi":"10.5194/isprsannals-i-2-217-2012","title":"REAL TIME SEMANTIC INTEROPERABILITY IN AD HOC NETWORKS OF GEOSPATIAL DATA SOURCES: CHALLENGES, ACHIEVEMENTS AND PERSPECTIVES","year":2012,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre de Géomatique du Québec","funders":"","keywords":"Geospatial analysis; Computer science; Interoperability; Semantic interoperability; Geospatial PDF; Data science; Semantic heterogeneity; Semantic Web; World Wide Web; Geography; Ontology-based data integration","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":[0.01976281,0.0008177516,0.0009779936,0.002954617,0.002410988,0.01356566,0.003919814,0.00468238,0.00157072],"category_scores_gemma":[0.009179267,0.0005966292,0.001351886,0.005515456,0.008313062,0.02737198,0.006565975,0.004658639,0.0005112204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003678733,"about_ca_system_score_gemma":0.005042791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004633477,"about_ca_topic_score_gemma":0.002120017,"domain_scores_codex":[0.9916279,0.004093282,0.0006798084,0.0008754536,0.002067575,0.0006558276],"domain_scores_gemma":[0.9920411,0.003698913,0.0006429709,0.00107999,0.001898107,0.0006390025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000255181,0.00006965725,0.0005862857,0.0004004952,0.00004201934,0.00036573,0.001578393,0.007543139,0.0007938765,0.9516636,0.002362179,0.0345691],"study_design_scores_gemma":[0.0000193654,0.00009970053,0.0005855547,0.0008893113,0.00005977444,0.0008206185,0.006469674,0.08358228,0.002464742,0.7443579,0.1605298,0.0001212443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0252197,0.03668358,0.8677293,0.03361828,0.001385935,0.0002962918,0.000132595,0.0004492436,0.03448508],"genre_scores_gemma":[0.471181,0.03777033,0.4782481,0.002714928,0.00108064,0.0004270828,0.0007271327,0.0001776443,0.007673101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01976281,"threshold_uncertainty_score":0.104517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124411373095551,"score_gpt":0.3391288539319795,"score_spread":0.2266877166224244,"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."}}