{"id":"W4229373717","doi":"10.36227/techrxiv.19597186.v1","title":"Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN","year":2022,"lang":"en","type":"preprint","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Aloha; Computer science; Exploit; Network packet; Collision; Artificial neural network; Interference (communication); Channel (broadcasting); Protocol (science); Joint (building); Wireless sensor network; Energy consumption; Computer network; Wireless; Throughput; Artificial intelligence; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002741826,0.0002001054,0.0002236075,0.0002172714,0.00009563086,0.000100661,0.00007518593,0.0002097863,0.0003674992],"category_scores_gemma":[0.00002362045,0.0002193654,0.00004586487,0.0001446049,0.00000805618,0.00006523748,0.0001486458,0.0009410605,0.000004008725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003148094,"about_ca_system_score_gemma":0.00001889827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000177833,"about_ca_topic_score_gemma":0.0002886263,"domain_scores_codex":[0.9990324,0.00008468077,0.0002736953,0.0002719063,0.0001470673,0.000190185],"domain_scores_gemma":[0.9996556,0.0000591742,0.00005697464,0.0001613056,0.00001932492,0.00004763937],"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":[0.00001114929,0.000006010355,0.0007728405,0.000223312,0.000007834517,0.000002462483,0.00022033,0.9731711,0.001303409,0.000007516152,0.00001360419,0.02426042],"study_design_scores_gemma":[0.0002324032,0.00004012248,0.005393583,0.0001210375,0.000004553432,8.892475e-7,0.00005894609,0.9884386,0.003258351,0.0001283564,0.002075756,0.0002474078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3078326,0.0003840654,0.6763107,0.00003742196,0.00105531,0.009263656,0.000003974561,0.0007864138,0.00432585],"genre_scores_gemma":[0.9960662,0.00005267092,0.001015298,0.00001338983,0.00009432423,0.002599298,0.00003908934,0.0000468238,0.00007291397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6882336,"threshold_uncertainty_score":0.8945462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01945204675598532,"score_gpt":0.2415451642013226,"score_spread":0.2220931174453373,"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."}}