{"id":"W2136248236","doi":"10.1109/icc.2009.5198710","title":"Adaptive Time Synchronization for Wireless Sensor Networks with Self-Calibration","year":2009,"lang":"en","type":"article","venue":"","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Timestamp; Computer science; Synchronization (alternating current); Clock drift; Extrapolation; Wireless sensor network; Real-time computing; Scheme (mathematics); Time synchronization; Wireless; Network Time Protocol; Wireless network; Clock synchronization; Computer network; Telecommunications; Channel (broadcasting)","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":[0.000528288,0.0003494743,0.0003151847,0.000363922,0.0003537612,0.0002992986,0.0006085621,0.000397042,0.0009328048],"category_scores_gemma":[0.002766734,0.0001753469,0.000260917,0.0005695595,0.0003936621,0.0007977985,0.0005303174,0.0005516391,0.0002835325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003732239,"about_ca_system_score_gemma":0.000373227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008870279,"about_ca_topic_score_gemma":0.0007251446,"domain_scores_codex":[0.9996561,0.00009128806,0.00001832066,0.0000813071,0.0001352354,0.00001769175],"domain_scores_gemma":[0.9993568,0.0003058763,0.0001000449,0.0001034583,0.0001172631,0.00001654752],"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":[0.000154938,0.00004007734,0.00124459,0.0001715948,0.00004462189,0.0001471488,0.0001814372,0.6989387,0.02775509,0.04265719,0.002144852,0.2265197],"study_design_scores_gemma":[0.000007674174,0.00003055901,0.0002195311,0.000006288221,0.00000662877,0.00005116792,0.000008496782,0.9884271,0.002468881,0.00690312,0.001861797,0.000008836451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01066261,0.0005284112,0.9871123,0.00009470132,0.00008256814,0.00002375508,0.00001688085,0.0003221431,0.001156642],"genre_scores_gemma":[0.787264,0.001313665,0.2075482,0.0000938823,0.0002216683,0.0001720648,0.0001053128,0.0001001582,0.003181054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009328048,"threshold_uncertainty_score":0.003120542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005376928770903561,"score_gpt":0.1924650872460606,"score_spread":0.1870881584751571,"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."}}