{"id":"W2339724126","doi":"10.5539/mas.v10n6p50","title":"A Distributed Method to Reconstruct Connection in Wireless Sensor Networks by Using Genetic Algorithm","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Computer science; Connection (principal bundle); Field (mathematics); Wireless; Wireless network; Computer network; Key distribution in wireless sensor networks; Genetic algorithm; Distributed computing; Telecommunications; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001143477,0.000307253,0.0003451421,0.0003555386,0.000350071,0.0002785651,0.001593803,0.0001396559,0.000005590303],"category_scores_gemma":[0.00003139133,0.0002537845,0.0000493646,0.00279502,0.0003347763,0.0003751285,0.0005301968,0.0001982135,0.00001487034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000499554,"about_ca_system_score_gemma":0.0001534465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007146725,"about_ca_topic_score_gemma":0.00001258887,"domain_scores_codex":[0.9961292,0.0001277819,0.0004786572,0.001474341,0.0006865925,0.001103492],"domain_scores_gemma":[0.9981942,0.0002529666,0.0001739306,0.0008850813,0.000133549,0.0003602738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000695942,0.00003152565,0.00009755888,0.000001375783,0.000002813723,0.000007375159,0.0000799882,0.3158759,0.2566083,0.0009882229,0.00003028744,0.4262697],"study_design_scores_gemma":[0.0004298277,0.00002671568,0.000308462,0.00004478507,0.00000334777,0.00004773816,0.00001733647,0.9734566,0.02471614,0.0004725948,0.00007950657,0.0003969417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07626513,0.00002639207,0.9224244,0.00019945,0.0003853381,0.0003417999,0.00001132047,0.0002029975,0.0001432123],"genre_scores_gemma":[0.5820315,0.000004970355,0.4177007,0.0001556431,0.00004652499,0.00002534936,0.000001382233,0.00001558108,0.00001841306],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6575807,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294363859642878,"score_gpt":0.2479770790018701,"score_spread":0.2350334404054413,"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."}}