{"id":"W2117777550","doi":"10.1155/2013/915963","title":"A Relation Routing Scheme for Distributed Semantic Media Query","year":2013,"lang":"en","type":"article","venue":"The Scientific World JOURNAL","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hunan University of Science and Technology; Natural Science Foundation of Beijing Municipality; National Institute on Alcohol Abuse and Alcoholism; Max-Planck-Gesellschaft; Hunan University; National Natural Science Foundation of China; Sun Yat-sen University; Natural Science Foundation of Hunan Province; University of Ottawa","keywords":"Computer science; Scalability; Semantic computing; Routing (electronic design automation); Scheme (mathematics); Relation (database); Semantic query; Information retrieval; Distributed computing; Data mining; Computer network; Semantic Web; Database; Web search query; Search engine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00340872,0.000660749,0.001071634,0.001774426,0.001675836,0.002086875,0.001994304,0.001245488,0.00264253],"category_scores_gemma":[0.005951097,0.0003590047,0.0007596278,0.002056208,0.000937095,0.005128001,0.002226078,0.00116617,0.001104978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001753128,"about_ca_system_score_gemma":0.002008426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003849808,"about_ca_topic_score_gemma":0.003104076,"domain_scores_codex":[0.9971628,0.000698312,0.0004556585,0.0006245635,0.0008602406,0.0001983776],"domain_scores_gemma":[0.9964859,0.0008674734,0.0002606013,0.001496385,0.0007486512,0.0001409315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001126107,0.0003722549,0.00204635,0.0004902465,0.0001266749,0.0004129566,0.001302616,0.0816597,0.08321249,0.2882071,0.02173179,0.5193117],"study_design_scores_gemma":[0.0001617488,0.0003756857,0.0005427796,0.00003323146,0.0001166716,0.0006569707,0.0002803428,0.8762485,0.02834413,0.04984848,0.04321059,0.000180868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009848588,0.0002708843,0.9839812,0.0002401068,0.0001020826,0.000267472,0.0001806302,0.002677189,0.002431815],"genre_scores_gemma":[0.3373733,0.0004440804,0.6554462,0.0002467378,0.0001459646,0.000327489,0.0009622858,0.0002331554,0.004820721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003849808,"threshold_uncertainty_score":0.01802725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349512909464218,"score_gpt":0.2286894332662147,"score_spread":0.2051943041715725,"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."}}