{"id":"W2143686207","doi":"10.1109/tvt.2007.897213","title":"Architecture of Wireless Sensor Networks With Mobile Sinks: Sparsely Deployed Sensors","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wireless sensor network; Computer science; Maximization; Computer network; Energy consumption; Minification; Wireless; Scheduling (production processes); Utility maximization; Key distribution in wireless sensor networks; Wireless network; Distributed computing; Engineering; Mathematical optimization; Mathematics; Telecommunications; Electrical engineering","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":[0.0003269422,0.0004249248,0.0005254088,0.0009693536,0.0002346542,0.0000346373,0.00100469,0.0006909926,0.00000761256],"category_scores_gemma":[0.00000418409,0.0003715513,0.0001778818,0.002553193,0.0004991708,0.0001105682,0.00001106671,0.001120955,0.00001162697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009003335,"about_ca_system_score_gemma":0.00005279416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002576301,"about_ca_topic_score_gemma":0.000142536,"domain_scores_codex":[0.9972049,0.00009530516,0.0005369281,0.0008271013,0.0004872995,0.0008484442],"domain_scores_gemma":[0.9977251,0.0002479836,0.0002467788,0.001403661,0.000227146,0.0001492736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008430184,0.0003049902,0.00005223344,0.00001311865,0.0001067086,0.0002049505,0.00009875055,0.9462219,0.007255898,0.0008633385,0.0000118198,0.04478193],"study_design_scores_gemma":[0.001442899,0.001367069,0.00008900542,0.0001954462,0.00008936026,0.0007407475,0.0002288628,0.4923322,0.5014647,0.00008840005,0.001138959,0.0008222795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.397959,0.00007881806,0.6004353,0.0001918596,0.0002481449,0.0002778533,0.000002525783,0.0007499846,0.00005648828],"genre_scores_gemma":[0.9340804,0.00005075489,0.06553213,0.0001048425,0.00003647821,0.00004676586,0.000001655022,0.00006093386,0.00008610056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5361213,"threshold_uncertainty_score":0.9998736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008074613555109049,"score_gpt":0.2047339049472168,"score_spread":0.1966592913921077,"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."}}