{"id":"W4312996750","doi":"10.1109/tccn.2022.3222792","title":"DeepAir: Enabling Data-Driven Dynamic Spectrum Sharing via Scalable Forecasting","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Scalability; Deep learning; Autoencoder; Encoder; Wireless; Real-time computing; Data mining; Machine learning; Artificial intelligence; 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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007647788,0.0002783261,0.0002931427,0.0003220869,0.004299856,0.0003549152,0.001899725,0.00005408202,0.00003594431],"category_scores_gemma":[0.000006484907,0.0003306969,0.000103707,0.001163822,0.0001574012,0.0006505736,0.0004086957,0.001039823,0.000006378421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001798756,"about_ca_system_score_gemma":0.00006447739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008594486,"about_ca_topic_score_gemma":0.0006162605,"domain_scores_codex":[0.9975169,0.0003153142,0.000421326,0.0008585259,0.0003112658,0.0005766643],"domain_scores_gemma":[0.9967532,0.001063853,0.0001940794,0.00175997,0.00009174043,0.0001372223],"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":[0.0000385167,0.0002641556,0.000139055,0.00001359586,0.0001823802,0.00002913347,0.0007906873,0.01559307,0.00008545871,0.00038294,0.0000183748,0.9824626],"study_design_scores_gemma":[0.000505514,0.0001448087,0.00004063413,0.0001864286,0.00007948348,0.00021826,0.0003587399,0.9949999,0.00005117936,0.001267947,0.001771175,0.0003759205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004882118,0.002618355,0.9894061,0.0007393616,0.0005602009,0.000382619,0.00004530597,0.0003076863,0.001058268],"genre_scores_gemma":[0.9872247,0.002373602,0.009660476,0.0003898857,0.00009244814,0.00008021606,0.00005938725,0.00004163097,0.00007768111],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9823425,"threshold_uncertainty_score":0.9999145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0680490976705417,"score_gpt":0.2810674543612527,"score_spread":0.2130183566907111,"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."}}