{"id":"W2154208087","doi":"10.1109/icc.2011.5962518","title":"Distributed Saturation Degree Based TDMA Scheduling Algorithm for Target Tracking","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Time division multiple access; Computer science; Wireless sensor network; Scalability; Algorithm; Scheduling (production processes); Degree (music); Distributed algorithm; Heuristic; Wireless; Real-time computing; Distributed computing; Mathematical optimization; Mathematics; Computer network; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002783241,0.0001693712,0.0001565919,0.00008740547,0.0001938651,0.0001376591,0.0005971607,0.0001115859,0.00002700137],"category_scores_gemma":[0.00004927165,0.0001535342,0.00009936029,0.0003829389,0.00002967446,0.0004459995,0.00006789109,0.000116835,0.00001039385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004923279,"about_ca_system_score_gemma":0.00004906864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002360176,"about_ca_topic_score_gemma":0.00001300052,"domain_scores_codex":[0.9986305,0.00004577046,0.000265601,0.0004373747,0.0002179793,0.0004027749],"domain_scores_gemma":[0.9989968,0.0001500722,0.0001118228,0.0004380679,0.0002018583,0.0001013734],"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":[0.00002901978,0.0003764334,0.0005099541,0.00002548983,0.000050989,0.00002576116,0.0004992243,0.2781934,0.001932136,0.08270312,0.0007057115,0.6349487],"study_design_scores_gemma":[0.0004031284,0.000057394,0.0005789651,0.00001757755,0.000005233931,0.000002922045,0.00001571828,0.9633992,0.03441309,0.000431755,0.0004640639,0.0002109174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001991031,0.00004368611,0.9957823,0.0001480354,0.0005304795,0.0002093253,0.000008336941,0.0004455151,0.0008412619],"genre_scores_gemma":[0.3628851,7.656442e-7,0.6367707,0.0001544063,0.00007613825,0.00001810851,0.00004485757,0.00001098914,0.00003893437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6852058,"threshold_uncertainty_score":0.6260945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05619038288457593,"score_gpt":0.2425689969152553,"score_spread":0.1863786140306794,"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."}}