{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002228969,0.0004148089,0.0006601253,0.001964058,0.0008097069,0.002708758,0.002111403,0.00107423,0.002316935],"category_scores_gemma":[0.003613263,0.0002649419,0.0009906759,0.002060444,0.001395544,0.006030866,0.002090338,0.00098321,0.0004619732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002057823,"about_ca_system_score_gemma":0.001041854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003859354,"about_ca_topic_score_gemma":0.003056555,"domain_scores_codex":[0.9982343,0.0006213454,0.0001675413,0.0002589516,0.0006348385,0.00008302449],"domain_scores_gemma":[0.998557,0.0005006356,0.0002019419,0.0002301398,0.0003880556,0.000122266],"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":[0.0001327194,0.00008660365,0.001275172,0.000101542,0.00006035142,0.000329069,0.0003300507,0.2721974,0.001428747,0.6818493,0.002770531,0.0394386],"study_design_scores_gemma":[0.00001035362,0.00002582204,0.0001019028,0.00001734267,0.00001183625,0.00008925898,0.00006694133,0.8699986,0.0005866176,0.1243481,0.004733527,0.00000965157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01305267,0.0001911932,0.9809701,0.0003904864,0.00003999381,0.00008693034,0.0002204927,0.0003148425,0.004733258],"genre_scores_gemma":[0.6190211,0.0004556268,0.3747697,0.0002259824,0.00007275495,0.0003264637,0.0008593653,0.00009071501,0.00417822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003859354,"threshold_uncertainty_score":0.01493061,"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."}}