{"id":"W2130412147","doi":"10.1002/wcm.2333","title":"Improving routing in networks of Unmanned Aerial Vehicles: Reactive‐Greedy‐Reactive","year":2012,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Zone Routing Protocol; Wireless Routing Protocol; Computer network; Link-state routing protocol; Optimized Link State Routing Protocol; Dynamic Source Routing; Routing protocol; Distributed computing; Destination-Sequenced Distance Vector routing; Routing (electronic design automation)","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":[0.001249872,0.0002267927,0.0004037562,0.0001499588,0.0003533268,0.00009829432,0.001503705,0.0001446554,0.000001315394],"category_scores_gemma":[0.00004195647,0.000243459,0.0000720666,0.0006677043,0.0002032623,0.0007185668,0.002361233,0.0005514363,0.000001695061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109446,"about_ca_system_score_gemma":0.00005897501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005470093,"about_ca_topic_score_gemma":0.00005605892,"domain_scores_codex":[0.997853,0.0003733416,0.0006447854,0.0003583568,0.0001727187,0.0005977689],"domain_scores_gemma":[0.9965537,0.0009788488,0.0005195138,0.001680983,0.0001288203,0.000138125],"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":[0.00002364193,0.0005875648,0.0337688,0.00004437848,0.00005509582,0.000002024171,0.01105838,0.008796067,0.006872424,0.06117989,0.00001456506,0.8775972],"study_design_scores_gemma":[0.0005246659,0.00006664464,0.008371326,0.0001895699,0.00001109033,0.0000149134,0.0008824975,0.9888985,0.0004877527,0.00007150879,0.0002186473,0.0002629376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6412774,0.001656962,0.3555357,0.00005152342,0.0002535672,0.0004874932,0.000001533506,0.0001140374,0.0006218652],"genre_scores_gemma":[0.9809224,0.0001688561,0.01859784,0.00004416093,0.000160555,0.00006790797,0.000009367501,0.00002321004,0.000005724887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9801024,"threshold_uncertainty_score":0.9927968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01628271164653725,"score_gpt":0.2591445560905831,"score_spread":0.2428618444440458,"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."}}