{"id":"W2075783342","doi":"10.1007/s10922-006-9039-4","title":"Clustering in WSN with Latency and Energy Consumption Constraints","year":2006,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cluster analysis; Energy consumption; Latency (audio); Wireless sensor network; Distributed computing; Architecture; Cluster (spacecraft); Computer network; Artificial intelligence","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":[0.0006017782,0.0001156057,0.0002382996,0.0001440464,0.00007251582,0.0001660863,0.0001593132,0.00003945228,9.882768e-7],"category_scores_gemma":[9.280585e-7,0.00008943753,0.00002026731,0.0001757784,0.00005751503,0.0001809434,0.00009867835,0.0001061672,2.953592e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003488392,"about_ca_system_score_gemma":0.00000646293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003734354,"about_ca_topic_score_gemma":0.00005996685,"domain_scores_codex":[0.9988216,0.0001228821,0.0004277612,0.0001687617,0.0002278302,0.0002311697],"domain_scores_gemma":[0.9994137,0.00006026665,0.0002997438,0.0001279705,0.00004055831,0.00005774484],"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":[0.00002423569,0.00003834723,0.01559322,0.00008460106,0.00004754372,0.0004957655,0.00006446165,0.9091108,0.000007286648,0.04949936,0.0005920224,0.02444238],"study_design_scores_gemma":[0.001508324,0.000222176,0.04572978,0.001430142,0.00003265941,0.000984847,0.0001241988,0.9446282,0.00000244496,0.0002857122,0.004759008,0.0002925053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1629233,0.004885444,0.8261853,0.0001490831,0.0007273736,0.0001080949,2.366089e-7,0.00002585598,0.004995293],"genre_scores_gemma":[0.992297,0.0006886318,0.006558704,0.00003945988,0.0001866441,0.000002637785,4.047502e-7,0.000006355842,0.0002201854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8293737,"threshold_uncertainty_score":0.3647157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008560486810825288,"score_gpt":0.1914992771596255,"score_spread":0.1829387903488002,"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."}}