{"id":"W2163238871","doi":"10.1109/ism.2007.4412379","title":"Layered Clustering for Solar Powered Wireless Visual Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Cluster analysis; Network packet; Computer science; Energy consumption; Computer network; Key distribution in wireless sensor networks; Node (physics); Sensor node; Bandwidth (computing); Wireless; Real-time computing; Wireless network; Engineering; Electrical engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004883682,0.0002977886,0.000297366,0.00009861917,0.000121437,0.00007534493,0.0002058944,0.0002838465,0.00003903322],"category_scores_gemma":[0.00003124763,0.0003143879,0.000113949,0.0002288859,0.00003566979,0.0001381909,0.00005812375,0.00027324,0.00001285555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009607831,"about_ca_system_score_gemma":0.000009172066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001758838,"about_ca_topic_score_gemma":0.00024012,"domain_scores_codex":[0.9982396,0.00001654178,0.0004390806,0.0002906243,0.000159006,0.00085513],"domain_scores_gemma":[0.9990225,0.0004030671,0.00004754128,0.0002678742,0.00007021994,0.0001887601],"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":[0.00003976306,0.00001865363,0.0003346119,0.00005590462,0.00006384853,0.00001592526,0.00004862049,0.9750705,0.003201286,0.0002844673,0.001085611,0.01978077],"study_design_scores_gemma":[0.0005909606,0.00004610758,0.0006242207,0.0000635962,0.00001362211,0.00001626388,0.00005859575,0.9901908,0.004268392,0.00001486465,0.003696667,0.0004158878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1114381,0.000164254,0.8797199,0.00001402687,0.00127441,0.000241025,0.000002568423,0.001347327,0.005798418],"genre_scores_gemma":[0.9746611,0.00004029231,0.02327691,0.0001173338,0.0009541274,0.00002605325,0.00002762734,0.0001567654,0.0007398218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.863223,"threshold_uncertainty_score":0.9999308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009904057221642544,"score_gpt":0.2375154277399145,"score_spread":0.227611370518272,"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."}}