{"id":"W2114795108","doi":"10.1109/tmc.2007.70769","title":"Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Wireless sensor network; Energy consumption; Network packet; Cluster analysis; Computer network; Random access; Sink (geography); Robustness (evolution); Efficient energy use; Access control; Distributed computing; Data collection; Data access; Real-time computing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001745112,0.0004607453,0.0006090985,0.0007998534,0.0006640384,0.0005474579,0.001258297,0.0003927381,0.0003603416],"category_scores_gemma":[0.005458224,0.0004256062,0.0003321955,0.001240399,0.001034184,0.00128947,0.001088951,0.0004170481,0.00008397442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030744,"about_ca_system_score_gemma":0.0009245666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002464351,"about_ca_topic_score_gemma":0.002398298,"domain_scores_codex":[0.9990423,0.000315768,0.00004873569,0.0002025279,0.0002894742,0.0001011724],"domain_scores_gemma":[0.9973691,0.001547019,0.0002797958,0.0003374577,0.0003799883,0.00008664349],"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":[0.0003486341,0.0001202966,0.00265628,0.0001421695,0.00008585093,0.0001691757,0.0003181891,0.8913739,0.01881723,0.01970447,0.0007353756,0.06552847],"study_design_scores_gemma":[0.00002909079,0.0001416727,0.0008520065,0.000005904255,0.00002211773,0.00008829392,0.00005308992,0.9861199,0.004507964,0.007435637,0.0007287782,0.0000156036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1458555,0.0005189725,0.8514449,0.0001600229,0.00003319406,0.0001201453,0.00003786556,0.0004763465,0.001353075],"genre_scores_gemma":[0.9256391,0.0003095419,0.07300059,0.0000504034,0.00002575466,0.0001114374,0.00003495152,0.00003480102,0.0007934609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002464351,"threshold_uncertainty_score":0.009229183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03995602292960668,"score_gpt":0.2647742312259403,"score_spread":0.2248182082963337,"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."}}