{"id":"W4229377607","doi":"10.36227/techrxiv.19597186","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Aloha; Computer science; Network packet; Exploit; Collision; Interference (communication); Channel (broadcasting); Artificial neural network; Protocol (science); Wireless sensor network; Energy consumption; Capture effect; Joint (building); Computer network; Wireless; Real-time computing; Throughput; Artificial intelligence; Engineering; Telecommunications; Computer security","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":[0.0009329249,0.0006205716,0.0007703163,0.0004601172,0.0002983359,0.0005945312,0.001227426,0.0005551597,0.0007045513],"category_scores_gemma":[0.002530271,0.0003810873,0.0004481778,0.000490721,0.0006012932,0.0009751948,0.000850688,0.001037318,0.0001684962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009499324,"about_ca_system_score_gemma":0.001181129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01683946,"about_ca_topic_score_gemma":0.01070682,"domain_scores_codex":[0.9996226,0.00009222452,0.00001973038,0.00007496757,0.00009691999,0.00009350575],"domain_scores_gemma":[0.9992144,0.0004028542,0.00008805144,0.00004846726,0.000198315,0.00004788543],"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":[0.0001583288,0.00006095238,0.001379574,0.00002704691,0.00004286475,0.0000531146,0.00004437248,0.8970262,0.001520203,0.002524178,0.0006700214,0.09649305],"study_design_scores_gemma":[0.000002048286,0.000007925133,0.00005331489,0.000001070848,0.000002136822,0.000003434141,0.000003036903,0.9990483,0.0002741166,0.0005515811,0.00005121701,0.000001831137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1639406,0.00109304,0.8300922,0.0004732474,0.0001076206,0.0000511722,0.00007554873,0.001095503,0.003071098],"genre_scores_gemma":[0.931034,0.0002831428,0.06584355,0.0001231271,0.00003058779,0.00003889697,0.0001074891,0.00003163255,0.002507572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01683946,"threshold_uncertainty_score":0.03348285,"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."}}