{"id":"W2050707899","doi":"10.4236/wsn.2009.15056","title":"EasiSim: A Scalable Simulator for Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"Wireless Sensor Network","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Chinese Academy of Sciences","keywords":"Computer science; Scalability; Graphical user interface; Visualization; Wireless sensor network; Process (computing); Wireless; Scale (ratio); Distributed computing; Simulation; Operating system; Artificial intelligence","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":[0.001069458,0.001258452,0.001527915,0.0002982001,0.001205426,0.0008326113,0.002728213,0.0008621757,0.00003204952],"category_scores_gemma":[0.00008281753,0.001271906,0.0007374348,0.002572428,0.0002418807,0.0007589486,0.0003915882,0.000923779,0.0001064421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824568,"about_ca_system_score_gemma":0.0001598425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003134356,"about_ca_topic_score_gemma":0.0000294189,"domain_scores_codex":[0.9911645,0.0004309377,0.001472597,0.002385637,0.001111937,0.003434388],"domain_scores_gemma":[0.9935759,0.001379461,0.000715036,0.002785723,0.0006385306,0.0009054018],"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":[0.0001716829,0.0003032522,0.0004658738,0.00002956198,0.0001043158,0.00009449691,0.0001179138,0.9140954,0.0003812301,0.03555183,0.01129062,0.03739381],"study_design_scores_gemma":[0.001894193,0.0004175333,0.0006783967,0.0002634079,0.00007388736,0.00007640416,0.00003709041,0.9758538,0.0006553475,0.0007984063,0.01770316,0.001548353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015614,0.001115023,0.8863947,0.001997747,0.003304035,0.001609505,0.00001702575,0.002181132,0.001819433],"genre_scores_gemma":[0.9176266,0.0002466465,0.07224125,0.00348009,0.004094423,0.0001063625,0.00006679229,0.0001876878,0.00195011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8160653,"threshold_uncertainty_score":0.9989731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117243387809696,"score_gpt":0.2343565032028473,"score_spread":0.2226321644218777,"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."}}