{"id":"W2395718674","doi":"","title":"Practical Implementation Issues of Reinforcement Learning Based ALOHA for Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"International Symposium on Wireless Communication Systems","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Aloha; Reinforcement learning; Computer science; Wireless sensor network; Wireless; Computer network; Wireless network; Artificial intelligence; Telecommunications; Throughput","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":[0.002211275,0.0005586032,0.0006469517,0.0003328771,0.0008608284,0.001651852,0.001844756,0.001200295,0.006287837],"category_scores_gemma":[0.006739756,0.0003494932,0.000281718,0.0004557262,0.0008356845,0.001758204,0.001331884,0.001669268,0.00099426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030594,"about_ca_system_score_gemma":0.001340569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003258487,"about_ca_topic_score_gemma":0.003426273,"domain_scores_codex":[0.9981465,0.0008895439,0.00009026509,0.0001951181,0.0004587987,0.0002196484],"domain_scores_gemma":[0.9964331,0.002365922,0.0001051085,0.0003507651,0.0006816654,0.00006336589],"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":[0.0006602888,0.0002270353,0.0016813,0.0002853452,0.00006490701,0.0002945148,0.0004083685,0.5010702,0.01244873,0.1534227,0.00525618,0.3241805],"study_design_scores_gemma":[0.00003804305,0.00006018421,0.00008935895,0.00001838859,0.00001106343,0.00009501865,0.00005811438,0.9720345,0.002568881,0.02295162,0.002063782,0.00001098838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009193392,0.0003692758,0.9832253,0.0004386543,0.00009442373,0.00006407457,0.00001527383,0.0004003679,0.006199229],"genre_scores_gemma":[0.7530066,0.0003049241,0.241031,0.0001807832,0.0000771087,0.0001710836,0.00003543462,0.00005736476,0.005135702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006287837,"threshold_uncertainty_score":0.02103496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088410840459373,"score_gpt":0.3038919614669415,"score_spread":0.2830078530623478,"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."}}