{"id":"W4410540537","doi":"10.1007/s12083-025-02002-y","title":"Optimal hybrid energy-saving cluster head selection for wireless sensor networks: an empirical study","year":2025,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Wireless sensor network; Cluster (spacecraft); Computer science; Head (geology); Selection (genetic algorithm); Energy (signal processing); Computer network; Wireless; Telecommunications; Artificial intelligence; Mathematics; Statistics; Geology","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":[0.0009759475,0.0004123616,0.0004390613,0.0003419124,0.001112035,0.0006826364,0.0009201194,0.0001445908,0.000001484764],"category_scores_gemma":[0.00003147096,0.0004365649,0.0001056858,0.001891733,0.00005277813,0.0002123695,0.0004692775,0.0003167221,0.000005499296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001336891,"about_ca_system_score_gemma":0.00007218484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009515647,"about_ca_topic_score_gemma":0.0002849984,"domain_scores_codex":[0.9963875,0.0001920135,0.0005808664,0.001428604,0.0005297135,0.0008812469],"domain_scores_gemma":[0.9973706,0.0005259366,0.0001331278,0.0009169454,0.000644823,0.000408562],"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":[0.000042551,0.0003970589,0.004486527,0.00000846036,0.00005519776,0.000001298007,0.0003392451,0.8869271,0.00005165899,0.003075459,0.009898597,0.09471688],"study_design_scores_gemma":[0.0005090314,0.0002187112,0.001488784,0.00005152954,0.00004789707,0.00001280641,0.0001020681,0.8413218,0.00006262536,0.0001338773,0.1556319,0.0004190289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09524107,0.00009578796,0.8971098,0.004813893,0.0005887384,0.001305263,0.000005806917,0.0005392073,0.0003004523],"genre_scores_gemma":[0.9261068,0.000009959188,0.06526065,0.002431019,0.001653036,0.001757892,0.00005478901,0.00005427544,0.002671568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8318492,"threshold_uncertainty_score":0.9998086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901102807958396,"score_gpt":0.3128607785266782,"score_spread":0.2938497504470942,"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."}}