{"id":"W2032068881","doi":"10.1109/glocom.2011.6134430","title":"An Efficient Adaptive Backoff Algorithm for Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Exponential backoff; Computer science; Probabilistic logic; Node (physics); Algorithm; Channel (broadcasting); Wireless sensor network; Computer network; Wireless; Reliability (semiconductor); Wireless network; Throughput; Power (physics); Telecommunications","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":[0.000927123,0.000561827,0.0007920411,0.0006982018,0.0006556563,0.000521103,0.001243551,0.000605404,0.0006811302],"category_scores_gemma":[0.002722689,0.0002721489,0.0002980767,0.0006221712,0.0003749564,0.001060198,0.0006691157,0.0009010792,0.0003182082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003540228,"about_ca_system_score_gemma":0.0007908337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288846,"about_ca_topic_score_gemma":0.001629509,"domain_scores_codex":[0.9992573,0.0001963699,0.00005386096,0.00008167919,0.00034797,0.00006290208],"domain_scores_gemma":[0.9992592,0.0003506099,0.00008117219,0.00008563049,0.0001865315,0.00003692012],"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":[0.0005386772,0.0002381337,0.001178409,0.000253295,0.00009874118,0.0001544156,0.0001376504,0.30862,0.03131846,0.02831678,0.00533323,0.6238122],"study_design_scores_gemma":[0.00004053895,0.00008886404,0.0001969909,0.000009892172,0.00001142827,0.00008927483,0.0000105011,0.987815,0.002828854,0.005266017,0.003627939,0.00001472733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007898111,0.0007742,0.9895703,0.00008005685,0.0001095517,0.00008712943,0.00001844691,0.0005130942,0.0009490903],"genre_scores_gemma":[0.409934,0.001148312,0.5851913,0.0001808924,0.000133845,0.0003483835,0.0001595936,0.00008827187,0.002815392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001288846,"threshold_uncertainty_score":0.004903138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03560222221027123,"score_gpt":0.2592096782702946,"score_spread":0.2236074560600233,"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."}}