{"id":"W1521857773","doi":"10.1007/978-3-642-05118-0_46","title":"Black Hole Search with Tokens in Interconnected Networks","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Network topology; Security token; Hypercube; Torus; Black hole (networking); Mobile agent; Topology (electrical circuits); Computer network; Theoretical computer science; Routing (electronic design automation); Parallel computing; Mathematics; Combinatorics","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":[0.001282166,0.0007316734,0.0006951257,0.001399848,0.0001514518,0.0007440834,0.004749446,0.0003207859,0.00003654998],"category_scores_gemma":[0.00002402348,0.0006410999,0.0001130034,0.001792202,0.0007567222,0.0006256274,0.001863248,0.001517636,0.00005672255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008189058,"about_ca_system_score_gemma":0.0004607443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005676733,"about_ca_topic_score_gemma":0.0007059689,"domain_scores_codex":[0.9944208,0.0001007874,0.000660319,0.002252173,0.001266849,0.001299092],"domain_scores_gemma":[0.9967158,0.0004181799,0.0002337648,0.002171939,0.0002095735,0.0002507062],"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":[0.00001130876,0.00002785166,0.00004634388,0.00001483983,0.000007951307,0.0003496554,0.0003090726,0.6112171,0.000003571513,0.004352486,0.00005193708,0.3836079],"study_design_scores_gemma":[0.0005409287,0.0004882041,0.0003982334,0.0007813176,0.000007201745,0.00001974524,3.916983e-7,0.986644,0.0001017221,0.008799468,0.001437799,0.0007810448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004967987,0.0002804614,0.9920633,0.0009162718,0.0006427616,0.0009582023,0.000001306771,0.0002156219,0.004425213],"genre_scores_gemma":[0.7031084,0.0001912637,0.287748,0.006895183,0.0008657636,0.0000363891,0.00001946306,0.0001004341,0.001035074],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7043154,"threshold_uncertainty_score":0.999604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454156507839086,"score_gpt":0.2305751706863052,"score_spread":0.2160336056079143,"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."}}