{"id":"W2146328109","doi":"10.1109/ccece.2006.277300","title":"Near-Optimal Node Clustering in Wireless Sensor Networks for Environment Monitoring","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Cluster analysis; Wireless sensor network; Computer science; Energy consumption; Node (physics); Data mining; Data stream clustering; CURE data clustering algorithm; Distributed computing; Computer network; Correlation clustering; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002556896,0.0002484407,0.0002487332,0.00008675039,0.0001724254,0.0002422752,0.000609644,0.0001423313,0.000005861227],"category_scores_gemma":[0.000003821734,0.0002526681,0.0000921524,0.0002470828,0.0000515308,0.0002477301,0.000313021,0.0001927445,0.000009189322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636359,"about_ca_system_score_gemma":0.00001542006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002230398,"about_ca_topic_score_gemma":0.00004717496,"domain_scores_codex":[0.9979047,0.00004915095,0.0004088463,0.0006209044,0.0002645172,0.0007518375],"domain_scores_gemma":[0.9990543,0.0001933061,0.00009496814,0.0005527911,0.00002198583,0.00008266782],"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":[0.0000104453,0.0000707509,0.004880751,0.00000560861,0.000004542458,0.00001678941,0.00004042416,0.9870653,0.0005377748,0.00209685,0.00007861326,0.005192186],"study_design_scores_gemma":[0.0005483117,0.00003280762,0.006017484,0.00003493563,0.000003033992,0.00000767385,0.00001631943,0.9904711,0.00164156,0.00001347412,0.0009143702,0.0002989438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2611369,0.00007069336,0.7372856,0.0001401774,0.0004439644,0.0001985935,4.92318e-7,0.0001698638,0.0005537673],"genre_scores_gemma":[0.7211437,0.00001577783,0.2778548,0.00004932086,0.0003264409,0.00004677783,0.000003544804,0.00002597149,0.0005335941],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4600069,"threshold_uncertainty_score":0.9999925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103063557416124,"score_gpt":0.2118099335440831,"score_spread":0.2015035778024707,"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."}}