{"id":"W2113505917","doi":"10.1109/icnsc.2005.1461238","title":"Geographic grid routing for wireless sensor networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Wireless sensor network; Computer network; Scalability; Routing protocol; Grid; Distributed computing; Routing (electronic design automation); Wireless Routing Protocol; Geographic routing; Protocol (science); Key distribution in wireless sensor networks; Data collection; Wireless; Wireless network; Telecommunications; Geography","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":[0.0004240647,0.0002533535,0.0002552471,0.0001432402,0.0003055451,0.0002414021,0.000992618,0.0001568023,0.000014718],"category_scores_gemma":[0.00002012045,0.0002316381,0.0001950109,0.0005911267,0.00005887215,0.0003702594,0.0002317018,0.0002068042,0.00002502545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004324728,"about_ca_system_score_gemma":0.00002306556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002498355,"about_ca_topic_score_gemma":0.00006511489,"domain_scores_codex":[0.9977949,0.00005870135,0.0003859004,0.0006557993,0.000276025,0.0008286722],"domain_scores_gemma":[0.9984541,0.000355653,0.0001336204,0.0007444813,0.0001407257,0.0001714404],"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":[0.000006550541,0.00006811901,0.0008319242,0.000004547955,0.00002270542,0.000003840542,0.00006508623,0.8419659,0.0001008777,0.08991637,0.001912798,0.06510124],"study_design_scores_gemma":[0.0004100386,0.0000429051,0.0003918716,0.00002061421,0.000007112747,0.00001585987,0.00001471743,0.9854079,0.0006645038,0.00004099125,0.0126599,0.0003235542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03304495,0.0001501547,0.96055,0.001371435,0.0009691954,0.0002635905,0.000001234922,0.0007699943,0.002879484],"genre_scores_gemma":[0.8177459,0.00003529694,0.178818,0.001173464,0.001308661,0.00003469497,0.000007983379,0.00002917621,0.0008468221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.784701,"threshold_uncertainty_score":0.9445929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000954941990677,"score_gpt":0.2218547009764939,"score_spread":0.2118451515565871,"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."}}