{"id":"W1995072035","doi":"10.1155/2013/538181","title":"Achieving Relative Time Synchronization in Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Journal of Control Science and Engineering","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Wireless sensor network; Synchronization (alternating current); Computer science; Distributed computing; Time synchronization; Discrete time and continuous time; Topology (electrical circuits); Convergence (economics); Key distribution in wireless sensor networks; Network topology; Real-time computing; Protocol (science); Wireless; Graph; Computer network; Wireless network; Engineering; Theoretical computer science; Mathematics; Telecommunications; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008220509,0.00008950849,0.0001784137,0.0003630794,0.0000740382,0.0002253867,0.0004892958,0.00004933239,0.000005435233],"category_scores_gemma":[0.0002939936,0.00007497024,0.00002019757,0.001124555,0.00008482059,0.002557874,0.00008463956,0.0002096306,0.000005809947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307377,"about_ca_system_score_gemma":0.00008387816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002915055,"about_ca_topic_score_gemma":2.135321e-7,"domain_scores_codex":[0.998993,0.00001775435,0.000295643,0.0001370513,0.0002965154,0.0002599803],"domain_scores_gemma":[0.9992031,0.0001260145,0.0001649925,0.0001292352,0.0003027468,0.00007390083],"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":[0.000003258974,0.00002176722,0.004794509,0.00001185568,0.00001922675,0.00002105208,0.0002838928,0.8526006,0.02732038,0.007495441,0.0001149617,0.1073131],"study_design_scores_gemma":[0.0003001679,0.00004841172,0.01175136,0.00007222933,0.000002574379,0.0000415674,0.00001172307,0.9874266,0.000110783,0.0001230063,0.000026972,0.00008460082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142669,0.0003019817,0.8843322,0.0007556823,0.0001345039,0.00009438068,4.798297e-8,0.00005007575,0.00006421362],"genre_scores_gemma":[0.9861863,0.0000494596,0.01364225,0.00004548831,0.00005771966,0.000002305766,3.630816e-8,0.00000473055,0.0000117163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8719195,"threshold_uncertainty_score":0.3057197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002165115250275683,"score_gpt":0.1679757130777121,"score_spread":0.1658105978274364,"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."}}