{"id":"W2164562671","doi":"10.1109/tmc.2009.42","title":"Large Connectivity for Dynamic Random Geometric Graphs","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Random graph; Random geometric graph; Wireless ad hoc network; Geometric networks; Spatial network; Torus; Wireless network; Theoretical computer science; Wireless; Mathematics; Combinatorics; Complex network; Graph; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008334554,0.0003117636,0.0004239293,0.0005304293,0.0007053806,0.000188102,0.0008102602,0.0001478892,0.0000167666],"category_scores_gemma":[0.00001713465,0.0003240556,0.0003743563,0.001838092,0.00003375465,0.0003683823,0.000005891299,0.0004237664,0.00002845442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001343722,"about_ca_system_score_gemma":0.00005690596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005359932,"about_ca_topic_score_gemma":0.00001553446,"domain_scores_codex":[0.9975459,0.0001344123,0.0004456361,0.0007950737,0.0003193819,0.0007595814],"domain_scores_gemma":[0.9975399,0.001191042,0.0001643723,0.0007935287,0.0001440038,0.00016712],"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":[0.0000651753,0.0006473924,0.00001131899,0.0000191455,0.00004976495,0.000007713861,0.0002809574,0.3737288,0.0004007469,0.001864179,0.0001831866,0.6227416],"study_design_scores_gemma":[0.003087095,0.0006319897,0.0003263996,0.00004672009,0.00002696859,0.0000262609,0.0000279402,0.9900747,0.002384204,0.002210878,0.0007542946,0.0004025372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05428703,0.0001754938,0.9419444,0.0001050184,0.001395019,0.001285686,0.00001553666,0.0006444205,0.0001474611],"genre_scores_gemma":[0.9807641,0.00002865954,0.01849906,0.0004167398,0.00006222304,0.0001276888,0.0000030022,0.00002349124,0.00007503812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9264771,"threshold_uncertainty_score":0.9999211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022450217242397,"score_gpt":0.2590516203199671,"score_spread":0.2488271181475432,"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."}}