{"id":"W1542789490","doi":"10.1007/0-387-23150-1_30","title":"Randomized Routing Algorithms in Mobile Ad Hoc Networks","year":2005,"lang":"en","type":"book-chapter","venue":"IFIP International Federation for Information Processing/IFIP","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Destination-Sequenced Distance Vector routing; Link-state routing protocol; Wireless Routing Protocol; Wireless ad hoc network; Heuristic; Computer network; Distributed computing; Optimized Link State Routing Protocol; Dynamic Source Routing; Greedy algorithm; Algorithm; Static routing; Routing (electronic design automation); Routing protocol; Artificial intelligence; Telecommunications","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001896627,0.0006932299,0.0009021371,0.0007748022,0.0005027495,0.002925873,0.001387949,0.0007234037,0.0001670548],"category_scores_gemma":[0.0002238707,0.0007114189,0.0004171471,0.0001848202,0.0001248825,0.006771885,0.0002893099,0.0007713441,0.000182257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006890728,"about_ca_system_score_gemma":0.0004280191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003727932,"about_ca_topic_score_gemma":0.0000412035,"domain_scores_codex":[0.9950463,0.00006097873,0.002517475,0.0006845661,0.001147413,0.0005432576],"domain_scores_gemma":[0.9954279,0.0004605911,0.002022962,0.000501135,0.001434722,0.0001526782],"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":[0.001439352,0.00003999693,0.000001585928,0.00007698981,0.00009477873,0.00000264708,0.001039506,0.05733011,0.000001030504,0.235555,0.00298085,0.7014382],"study_design_scores_gemma":[0.01659312,0.0000437504,0.000001907453,0.0003472579,0.00002050646,0.00002189065,0.00002227226,0.6324028,0.00001752806,0.007509653,0.3424989,0.0005203782],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000006674008,0.002129992,0.8157716,0.0008503324,0.002041254,0.002592869,0.00004292965,0.000370105,0.1761942],"genre_scores_gemma":[0.1328492,0.007547273,0.2667308,0.01371791,0.009344091,0.01273265,0.01563004,0.000592562,0.5408555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7009178,"threshold_uncertainty_score":0.9995337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439476128446215,"score_gpt":0.2618405975754782,"score_spread":0.247445836291016,"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."}}