{"id":"W2069254940","doi":"10.1016/j.jpdc.2013.08.009","title":"Searching for a black hole in interconnected networks using mobile agents and tokens","year":2013,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Ontario Tech University","funders":"","keywords":"Asynchronous communication; Security token; Computer science; Network topology; FIFO (computing and electronics); Hypercube; Focus (optics); Mathematical proof; Torus; Whiteboard; Computer network; Distributed computing; Topology (electrical circuits); Theoretical computer science; Mathematics; Combinatorics; Parallel computing; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006398534,0.0001550272,0.0002966236,0.0001550742,0.0001276056,0.0003387111,0.000345616,0.00005019148,0.000004099615],"category_scores_gemma":[0.00004410225,0.0001384728,0.0000682867,0.0002870073,0.00004536262,0.0004110783,0.0004093968,0.000238194,7.451198e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008602865,"about_ca_system_score_gemma":0.00003175599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005992212,"about_ca_topic_score_gemma":0.00000571398,"domain_scores_codex":[0.9985233,0.0001151964,0.0005509829,0.0002420959,0.0001675188,0.0004008915],"domain_scores_gemma":[0.9989403,0.0002624546,0.000343763,0.0001540364,0.0001314127,0.0001680046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001786767,0.00005200321,0.004441017,0.00004077637,0.00004208792,0.00003271194,0.0003795445,0.9662477,0.00004500894,0.0004959102,0.0006906061,0.02751473],"study_design_scores_gemma":[0.001199461,0.0001723533,0.010142,0.0001880233,0.00001269997,0.00002921695,0.000237769,0.9869956,0.000005121198,0.0006125872,0.0002639795,0.0001412217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4756667,0.0002001487,0.5236933,0.00009088354,0.00008274624,0.0002468021,9.514501e-7,0.00001061942,0.000007903812],"genre_scores_gemma":[0.963631,0.00004727497,0.03600724,0.0001718474,0.0001193042,0.000006024832,0.000004667051,0.000008252945,0.000004370396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4879643,"threshold_uncertainty_score":0.5646757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328991508059717,"score_gpt":0.2744323974459021,"score_spread":0.2511424823653049,"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."}}