{"id":"W1589374005","doi":"10.1109/wowmom.2006.116","title":"Wireless Sensor Networks: To Cluster or Not To Cluster?","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Wireless sensor network; Computer science; Cluster analysis; Maximization; Cluster (spacecraft); Node (physics); Computer network; Sink (geography); Distributed computing; Key distribution in wireless sensor networks; Data mining; Wireless; Wireless network; Artificial intelligence; Mathematical optimization; Mathematics; Telecommunications; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004157973,0.0004337372,0.0004281284,0.0002854246,0.0002318061,0.0004930431,0.001686517,0.0002035991,0.00006490586],"category_scores_gemma":[0.00003143766,0.0003457407,0.0001306344,0.001634732,0.00004172495,0.0003113857,0.001061334,0.0002439465,0.0006569566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142809,"about_ca_system_score_gemma":0.00005021598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003112674,"about_ca_topic_score_gemma":0.001072756,"domain_scores_codex":[0.9962611,0.0001645483,0.0006105392,0.001134573,0.0006601495,0.00116913],"domain_scores_gemma":[0.9973924,0.0004131344,0.0001012955,0.001440156,0.0001920301,0.000460992],"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":[0.0001095921,0.00009360573,0.00008961903,0.000005696555,0.00001003314,0.00004383676,0.0001388007,0.928211,0.0002579473,0.007951072,0.05376838,0.009320372],"study_design_scores_gemma":[0.0006130802,0.0001877215,0.0009545519,0.0000590093,0.000007460759,0.00004747325,0.00002952872,0.9683557,0.002073067,0.00001977319,0.02696337,0.0006892847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1098373,0.00001159777,0.8752072,0.006316702,0.001288531,0.0005310094,0.000001916756,0.0007413099,0.006064408],"genre_scores_gemma":[0.7815549,0.000003624112,0.1684573,0.02216349,0.001037629,0.00004732721,0.000005920758,0.00006275448,0.02666705],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.70675,"threshold_uncertainty_score":0.9998994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136537735758617,"score_gpt":0.2406438099951116,"score_spread":0.2292784326375255,"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."}}