{"id":"W2118766926","doi":"10.1109/ds-rt.2011.14","title":"Error-Resilient Routing for Supporting Multi-dimensional Range Query in HD Tree","year":2011,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Routing table; Routing (electronic design automation); Static routing; Multipath routing; Distributed computing; Tree (set theory); Destination-Sequenced Distance Vector routing; Link-state routing protocol; Dynamic Source Routing; Range (aeronautics); Computer network; Routing protocol; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000655646,0.0001169481,0.0001555494,0.0001100207,0.00009152012,0.00003781066,0.0003761874,0.00005199472,0.00002522832],"category_scores_gemma":[0.00007466395,0.000101684,0.000094535,0.0001173592,0.00001629558,0.0002806892,0.0001752747,0.0001133218,0.00003028162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000372077,"about_ca_system_score_gemma":0.00004516338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001092196,"about_ca_topic_score_gemma":0.0006501097,"domain_scores_codex":[0.9987444,0.00004300391,0.0003297002,0.0003597423,0.0001633461,0.0003597648],"domain_scores_gemma":[0.9993899,0.0001237778,0.00009008923,0.0002765508,0.00004893804,0.000070686],"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":[0.0004570328,0.002834237,0.3694001,0.0001532467,0.0001546772,0.0005247626,0.02382512,0.001636928,0.03253011,0.1511919,0.01003627,0.4072556],"study_design_scores_gemma":[0.001566517,0.00009854213,0.03341047,0.00005129278,0.000006212791,0.00001682153,0.0003689727,0.962171,0.001473593,0.0004221647,0.0001051039,0.0003093656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6963089,0.00006521055,0.2998241,0.0002595779,0.0004719323,0.0002937234,0.00000235559,0.0002116631,0.002562593],"genre_scores_gemma":[0.9581963,5.220447e-7,0.04007912,0.0004763478,0.0000301432,0.00002218159,0.000002371838,0.000007755929,0.001185274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.960534,"threshold_uncertainty_score":0.4146553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0807388463711615,"score_gpt":0.2826802693643938,"score_spread":0.2019414229932323,"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."}}