{"id":"W1917544871","doi":"10.1111/j.1467-9671.2009.01172.x","title":"SIM‐NET: A View‐Based Semantic Similarity Model for<i>Ad Hoc</i>Networks of Geospatial Databases","year":2009,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Natural Resources Canada; Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semantic similarity; Computer science; Semantic interoperability; Information retrieval; Semantic computing; Semantic integration; Semantic grid; Geospatial analysis; Semantic heterogeneity; Explicit semantic analysis; Similarity (geometry); Semantic technology; Interoperability; Artificial intelligence; Semantic Web; Geography; World Wide Web; Ontology-based data integration","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002470096,0.0001611636,0.0002990399,0.0001518269,0.0001053652,0.00003097199,0.0005180776,0.00008145698,0.00001516768],"category_scores_gemma":[0.00002373163,0.0001561054,0.0001301307,0.0003913441,0.00006437994,0.000339668,0.000009968061,0.0001649651,0.000001310803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002998449,"about_ca_system_score_gemma":0.0001025732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009074561,"about_ca_topic_score_gemma":0.001558656,"domain_scores_codex":[0.9987491,0.00004428207,0.0003550275,0.000353124,0.0001848576,0.0003135655],"domain_scores_gemma":[0.9989762,0.0002182796,0.00008080646,0.0006097126,0.00006558864,0.00004943643],"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":[0.00008621379,0.0006047547,0.0002027337,0.00007222726,0.00002346166,0.000008903242,0.0007438002,0.7998417,0.0004006695,0.001878885,0.0001794634,0.1959572],"study_design_scores_gemma":[0.0006701358,0.0001123247,0.001844347,0.0000501558,0.00002759801,0.000002752799,0.0000196792,0.9941999,0.0009466974,0.001600107,0.0003567432,0.0001695741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008033497,0.0006378504,0.9894738,0.001144917,0.0001752977,0.0002985791,0.00003454626,0.0001229557,0.0000785667],"genre_scores_gemma":[0.8512909,0.0001713982,0.1480828,0.0003639918,0.0000138631,0.00002374382,0.000009067272,0.000006045798,0.00003815729],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8432574,"threshold_uncertainty_score":0.6365795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0437618308101056,"score_gpt":0.2916061027864797,"score_spread":0.2478442719763742,"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."}}