{"id":"W4362502915","doi":"10.1007/978-3-031-29419-8_16","title":"Reinforcement Learning Aided Routing in Tactical Wireless Sensor Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Wireless sensor network; Computer network; Network packet; Geographic routing; Routing protocol; Key distribution in wireless sensor networks; Dynamic Source Routing; Real-time computing; Wireless; Wireless network; 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":[0.000458484,0.0003473006,0.0004998911,0.0001931984,0.0001392166,0.0004285653,0.0007420024,0.0005266088,0.002035113],"category_scores_gemma":[0.0011771,0.0001926109,0.0001815169,0.0004344604,0.0003740676,0.0005686076,0.0004416624,0.0009096365,0.0004184956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004040466,"about_ca_system_score_gemma":0.0003136837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115933,"about_ca_topic_score_gemma":0.001339694,"domain_scores_codex":[0.9997898,0.00008057927,0.000007876705,0.00002844081,0.00007264072,0.00002076621],"domain_scores_gemma":[0.9994998,0.0003454435,0.00002773354,0.00004217466,0.0000700243,0.00001478421],"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":[0.00004785928,0.00003784238,0.0001519285,0.00006719255,0.00001619368,0.00002814262,0.00002528085,0.796958,0.002731996,0.01940639,0.003012255,0.1775169],"study_design_scores_gemma":[0.00000285655,0.00002160062,0.00006476169,0.000004597297,0.000002291019,0.00001515521,0.000003262743,0.9911607,0.0004852365,0.00656438,0.001672872,0.000002366452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01064046,0.002373552,0.9778055,0.0002574744,0.0002021969,0.00001966619,0.00002221477,0.0004186726,0.008260258],"genre_scores_gemma":[0.7230512,0.003420335,0.2424954,0.0001505574,0.0002988374,0.00009402668,0.0001062359,0.0001545354,0.0302287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002035113,"threshold_uncertainty_score":0.006808102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696218349917092,"score_gpt":0.2395051527402027,"score_spread":0.2225429692410318,"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."}}