{"id":"W2288710413","doi":"10.1109/glocom.2015.7417534","title":"Energy-Efficient and Fault-Tolerant Evolution Models for Large-Scale Wireless Sensor Networks: A Complex Networks-Based Approach","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Fault tolerance; Computer science; Distributed computing; Network topology; Efficient energy use; Energy (signal processing); Node (physics); Cluster analysis; Topology (electrical circuits); Computer network; Engineering; Artificial intelligence; Mathematics","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":[0.000700069,0.000461009,0.0006683301,0.00009683325,0.0006240369,0.0002597553,0.001346959,0.000157651,0.00002091155],"category_scores_gemma":[0.000005886082,0.000474259,0.0002433043,0.0006500377,0.0002960769,0.0001929563,0.0004872458,0.00029027,0.000004379963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002962503,"about_ca_system_score_gemma":0.0002834937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001324053,"about_ca_topic_score_gemma":0.0006130633,"domain_scores_codex":[0.9971851,0.0004312534,0.0006862787,0.0006261854,0.0003295562,0.0007416708],"domain_scores_gemma":[0.9959658,0.0001662351,0.0003950915,0.002146038,0.0009385059,0.000388376],"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":[0.0000830303,0.0008713566,0.002740264,0.0000103256,0.0001359681,1.911148e-7,0.0001119857,0.7358096,0.000009155448,0.2397669,0.0139079,0.006553396],"study_design_scores_gemma":[0.001201863,0.00005803101,0.0001860752,0.00005005167,0.0001509736,0.000002220319,0.0006869839,0.9816049,0.000004194771,0.00978451,0.00577503,0.0004951183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004206809,0.0007456958,0.9860876,0.0004431937,0.000101835,0.0007361696,0.0003614914,0.0002456096,0.007071578],"genre_scores_gemma":[0.9481782,0.00003604492,0.04966175,0.0001383879,0.0001901296,0.0004560414,0.001244586,0.00003316733,0.00006168357],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9439714,"threshold_uncertainty_score":0.9997709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0657653160746157,"score_gpt":0.3090291236470349,"score_spread":0.2432638075724192,"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."}}