{"id":"W2565682558","doi":"10.1109/nbis.2016.81","title":"Improved Genetic Algorithm Based Energy Efficient Routing in Two-Tiered Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Alberta","funders":"","keywords":"Relay; Computer network; Wireless sensor network; Computer science; Geographic routing; Key distribution in wireless sensor networks; Node (physics); Network packet; Routing (electronic design automation); Mobile wireless sensor network; Static routing; Energy consumption; Routing protocol; Wireless; Wireless network; Engineering; Telecommunications","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":[0.0006000404,0.0006017427,0.0006950384,0.0006293587,0.0003927323,0.0007477422,0.001013801,0.0008469642,0.0006628793],"category_scores_gemma":[0.00109045,0.0002995984,0.0005736404,0.0008187512,0.0004529717,0.0007432494,0.000620966,0.000520526,0.0001273428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007036469,"about_ca_system_score_gemma":0.001076051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006763947,"about_ca_topic_score_gemma":0.006437123,"domain_scores_codex":[0.999472,0.0001959949,0.00002469124,0.00008042539,0.0001661359,0.00006083206],"domain_scores_gemma":[0.99972,0.0001125738,0.00003816611,0.00002880602,0.00008272483,0.00001770068],"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":[0.00003853821,0.00004402654,0.0005209009,0.00004857231,0.00004815398,0.0000918372,0.00004340408,0.9507568,0.00509525,0.007450926,0.0005632203,0.03529839],"study_design_scores_gemma":[0.00001308644,0.00004380791,0.0001377141,0.000003682649,0.00001038979,0.00003516544,0.000007529775,0.9963853,0.0005752395,0.002234434,0.0005460857,0.000007453728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04416721,0.0006891686,0.9517764,0.0002506654,0.00009281611,0.00005921543,0.00004701172,0.000390156,0.002527402],"genre_scores_gemma":[0.6219898,0.0006661026,0.3730805,0.0002302516,0.0000298229,0.0001820578,0.0001677576,0.00005384566,0.003599955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006763947,"threshold_uncertainty_score":0.01344913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006290887180162713,"score_gpt":0.2106550818183816,"score_spread":0.2043641946382189,"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."}}