{"id":"W3174419877","doi":"10.3390/en14133935","title":"FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Wireless sensor network; Cluster analysis; Computer science; Fault tolerance; Efficient energy use; Key distribution in wireless sensor networks; Wireless; Distributed computing; Node (physics); Hierarchical clustering; Algorithm; Computer network; Wireless network; Engineering; Telecommunications; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005842856,0.0006693473,0.0005828822,0.0009833093,0.001120721,0.0004976504,0.001592573,0.001096714,0.00140809],"category_scores_gemma":[0.002240586,0.0002339885,0.0004581873,0.0009563818,0.0004418468,0.001143471,0.0009526369,0.0008623292,0.0005164081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001201,"about_ca_system_score_gemma":0.001431409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008803541,"about_ca_topic_score_gemma":0.01059527,"domain_scores_codex":[0.9995516,0.00009416388,0.00002536744,0.00007290535,0.0002073237,0.00004864628],"domain_scores_gemma":[0.999564,0.000125024,0.00005439844,0.0000630785,0.0001666605,0.00002675429],"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":[0.0001855131,0.0001006434,0.001010741,0.0001709105,0.00008512245,0.00009363228,0.0002106443,0.5724982,0.01345454,0.01401835,0.00822253,0.3899492],"study_design_scores_gemma":[0.00003199754,0.00008125067,0.0003382427,0.00001466559,0.00001175672,0.0000916852,0.00003414879,0.9824426,0.004437151,0.006663619,0.00583078,0.00002212713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01120242,0.0006988043,0.9846098,0.0002893186,0.00009155009,0.0001728492,0.00009796875,0.001041919,0.00179531],"genre_scores_gemma":[0.310674,0.0004704744,0.6831632,0.0002393568,0.00005873484,0.0003801592,0.0004681935,0.0001219776,0.004423976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008803541,"threshold_uncertainty_score":0.01750463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01060054146118589,"score_gpt":0.225615228560726,"score_spread":0.2150146870995401,"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."}}