{"id":"W2000361393","doi":"10.1109/iccnc.2013.6504241","title":"Multi-objective QoS routing for wireless sensor networks","year":2013,"lang":"en","type":"article","venue":"2013 International Conference on Computing, Networking and Communications (ICNC)","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer network; Quality of service; Routing protocol; Wireless sensor network; Distributed computing; Wireless Routing Protocol; Reliability (semiconductor); Routing (electronic design automation); Geographic routing; Dynamic Source Routing","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":[0.000560128,0.0003963891,0.0003685954,0.0002134442,0.0009530229,0.0009569448,0.003132711,0.0001984788,0.0000204947],"category_scores_gemma":[0.00005262961,0.0004021733,0.0001410315,0.0003692105,0.0002432584,0.0003568376,0.001387739,0.000641548,0.00004135036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245458,"about_ca_system_score_gemma":0.00006596095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003156811,"about_ca_topic_score_gemma":0.00009252855,"domain_scores_codex":[0.9972758,0.0002981952,0.0006410957,0.0007811949,0.0003532989,0.0006504739],"domain_scores_gemma":[0.9957332,0.001107817,0.0005275617,0.001589637,0.0008552944,0.0001865149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003215859,0.0005892763,0.002714162,0.00001650832,0.0003156655,0.000003315867,0.001800277,0.271322,0.0003268978,0.4689796,0.005966282,0.2479339],"study_design_scores_gemma":[0.0006457209,0.0000810343,0.001587508,0.0002813508,0.00001178442,0.00001646312,0.000198332,0.9920782,0.00002093372,0.000523099,0.004117943,0.0004376662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01334825,0.0003667611,0.972091,0.004620396,0.002319631,0.0007520692,0.00000514535,0.0004656521,0.006031103],"genre_scores_gemma":[0.8817081,0.0006150996,0.1156226,0.000617784,0.0006905656,0.0001315221,0.00006057842,0.0000373621,0.0005163231],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8683599,"threshold_uncertainty_score":0.999843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05600681202174877,"score_gpt":0.2950054410584486,"score_spread":0.2389986290366999,"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."}}