{"id":"W2787234235","doi":"10.23977/jnca.2017.21001","title":"An Improved Data Fusion Method IICKPAD for Privacy Protection in Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Network Computing and Applications","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sensor fusion; Wireless sensor network; Computer science; Data redundancy; Redundancy (engineering); Fusion; Process (computing); Node (physics); Computer network; Wireless; Data mining; Privacy protection; Computer security; Artificial intelligence; Engineering; Telecommunications; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.002235273,0.0001819739,0.0003700833,0.00009337656,0.001020276,0.0006076444,0.002442643,0.0001506663,3.989984e-7],"category_scores_gemma":[0.00007037284,0.0001718623,0.00006443517,0.0002376617,0.00006524385,0.0007101219,0.0006612191,0.0005158246,3.430027e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004097555,"about_ca_system_score_gemma":0.0000592183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003169494,"about_ca_topic_score_gemma":0.00002273213,"domain_scores_codex":[0.9981079,0.0001759329,0.0006604045,0.0004901472,0.0001765581,0.0003891212],"domain_scores_gemma":[0.996245,0.0004095703,0.001245797,0.001690832,0.0002505234,0.0001582333],"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":[0.00004488263,0.0001610255,0.0008998981,0.00002843072,0.00003347486,0.000003997103,0.0002520707,0.1379,0.000437284,0.004473247,0.000311487,0.8554542],"study_design_scores_gemma":[0.0006644376,0.000130233,0.003103624,0.0001288551,0.00001974174,0.0001093153,0.00002638756,0.9899377,0.0000310697,0.00272361,0.002941738,0.0001832689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02484296,0.0002390726,0.972748,0.0009812228,0.0003885234,0.000728967,0.00000273424,0.00005072787,0.00001781496],"genre_scores_gemma":[0.6895723,0.0001087638,0.3079562,0.00007983032,0.002237761,0.00001711368,0.000007742125,0.00001577886,0.000004428034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8552709,"threshold_uncertainty_score":0.7847245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04658887744468047,"score_gpt":0.3502391232046969,"score_spread":0.3036502457600164,"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."}}