{"id":"W2593862813","doi":"10.1007/s11276-017-1483-4","title":"E3TX: an energy-efficient expected transmission count routing decision strategy for wireless sensor networks","year":2017,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Energy consumption; Wireless sensor network; Network packet; Routing protocol; Computer network; Transmission (telecommunications); Efficient energy use; Real-time computing; Telecommunications; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001280522,0.0007824115,0.0007733127,0.0005872096,0.0004115834,0.0007037467,0.001899371,0.0008678251,0.002923504],"category_scores_gemma":[0.003100609,0.0002202096,0.0003413538,0.0005616851,0.0003836771,0.001117654,0.001236378,0.0007629977,0.000350918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006961978,"about_ca_system_score_gemma":0.0009899423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00152211,"about_ca_topic_score_gemma":0.002808538,"domain_scores_codex":[0.9994356,0.0001863706,0.0000293522,0.0000876391,0.0001803125,0.00008083507],"domain_scores_gemma":[0.9991013,0.0005390843,0.00006618181,0.00005877071,0.0001715346,0.00006310705],"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":[0.0007182651,0.0003045621,0.0008393078,0.0001897173,0.00009411619,0.0001581341,0.0001296859,0.6394694,0.01197467,0.02904875,0.008798911,0.3082744],"study_design_scores_gemma":[0.00002186208,0.0001056353,0.00008634473,0.000005739675,0.00001061728,0.00003610196,0.00001411356,0.9933235,0.001428024,0.004268307,0.0006929156,0.000006823031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02012621,0.0004000299,0.9753559,0.000357339,0.0001181952,0.0001107821,0.0001005995,0.0006096317,0.002821337],"genre_scores_gemma":[0.8115602,0.0003332598,0.1815941,0.0003741527,0.0001144,0.0002166539,0.0001896674,0.0001356727,0.00548191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002923504,"threshold_uncertainty_score":0.009780169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222404356618065,"score_gpt":0.2690684449742435,"score_spread":0.246828009312437,"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."}}