{"id":"W2804747094","doi":"10.1177/1550147718774467","title":"An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks","year":2018,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Cognitive radio; Synchronization (alternating current); Robustness (evolution); Data synchronization; Process (computing); Anomaly detection; Node (physics); Distributed computing; Wireless; Real-time computing; Time synchronization; Computer network; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001012155,0.0006001593,0.0007079233,0.001060252,0.0007476125,0.0006866143,0.001248827,0.0006854911,0.0006950509],"category_scores_gemma":[0.00411122,0.0002277033,0.0005030837,0.001184848,0.0006531862,0.001406904,0.0008142774,0.001082662,0.0002689991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006500541,"about_ca_system_score_gemma":0.001134851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002603699,"about_ca_topic_score_gemma":0.00177864,"domain_scores_codex":[0.9988039,0.0002229811,0.00008367136,0.0003023277,0.0005176771,0.00006938015],"domain_scores_gemma":[0.9986346,0.0004048174,0.0001494094,0.000142335,0.0006116643,0.00005711751],"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":[0.0003859548,0.0001383008,0.004002795,0.0001934682,0.0001346194,0.0002729536,0.0004496602,0.1266552,0.04764948,0.02909574,0.003252067,0.7877697],"study_design_scores_gemma":[0.00003156077,0.0001019121,0.0006513779,0.000008668798,0.00003451449,0.0002851573,0.00003362275,0.9797763,0.0120217,0.004783041,0.002240078,0.00003224777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006199353,0.0001332828,0.9929046,0.00005097458,0.00005528333,0.00002185936,0.000007564976,0.0002773014,0.0003497773],"genre_scores_gemma":[0.4847806,0.0004728635,0.5121593,0.0001387938,0.0001460685,0.0001727653,0.0001022054,0.00008575023,0.001941654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002603699,"threshold_uncertainty_score":0.005352914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009065849009982687,"score_gpt":0.2789788236225247,"score_spread":0.269912974612542,"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."}}