{"id":"W2894175859","doi":"10.1109/icis.2018.8466476","title":"Coverage Optimization of Wireless Sensor Networks with Normal Distribution","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Wireless sensor network; Base station; Computer science; Metric (unit); Limit (mathematics); Software deployment; Focus (optics); Performance metric; Wireless; Computer network; Key distribution in wireless sensor networks; Wireless network; Quality of service; Distribution (mathematics); Mathematical optimization; Telecommunications; Mathematics; Engineering","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":[0.001621304,0.0008432496,0.0008162946,0.0008082842,0.0002530681,0.0006530057,0.0008237008,0.000696765,0.0006113828],"category_scores_gemma":[0.005679249,0.000384575,0.0005798836,0.000928387,0.0009845396,0.001060827,0.000902144,0.0006433661,0.0001180332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139682,"about_ca_system_score_gemma":0.0006201805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005819844,"about_ca_topic_score_gemma":0.002046023,"domain_scores_codex":[0.9991171,0.0004043526,0.00002600994,0.0001324969,0.0002220143,0.00009812846],"domain_scores_gemma":[0.9978585,0.001559516,0.0002005128,0.00005722883,0.000267929,0.0000563166],"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":[0.00002831568,0.00001295726,0.000395547,0.00003425381,0.0000148368,0.00003760177,0.00003089938,0.9877343,0.0007015009,0.00491902,0.0002196973,0.005871044],"study_design_scores_gemma":[0.000001960948,0.00001163872,0.00007083596,0.000001433342,0.000001850936,0.000006351714,0.000004813034,0.9976031,0.00008265609,0.002137575,0.00007592982,0.000001777744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06368488,0.0009633252,0.931433,0.0004109582,0.00003753966,0.00004027755,0.00007413363,0.0001457525,0.003210159],"genre_scores_gemma":[0.9516227,0.001124279,0.04303278,0.0001042562,0.0000489293,0.000127765,0.0001345155,0.00007065777,0.003734149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005819844,"threshold_uncertainty_score":0.01157194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005098083825559502,"score_gpt":0.1926365684202744,"score_spread":0.1875384845947149,"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."}}