{"id":"W1607945875","doi":"10.1109/isscs.2015.7203939","title":"Primary user activity prediction using the hidden Markov model in cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Cognitive radio; Hidden Markov model; Computer science; Frame (networking); Channel (broadcasting); Airfield traffic pattern; Markov model; Markov process; Spectrum (functional analysis); Markov chain; Computer network; Machine learning; Artificial intelligence; Speech recognition; Wireless; Telecommunications; Statistics; Mathematics","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":[0.0007274455,0.0001818585,0.0002084823,0.0000964128,0.0001496525,0.0002021818,0.0003132207,0.00009759758,0.000004207825],"category_scores_gemma":[0.00003831009,0.000135826,0.00006486223,0.0005675386,0.00007695145,0.0008487641,0.0002611449,0.0003633462,0.00000215248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002789312,"about_ca_system_score_gemma":0.0001933376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531729,"about_ca_topic_score_gemma":0.0001383938,"domain_scores_codex":[0.9984239,0.0002069278,0.0002005198,0.0004327542,0.0003222036,0.0004136844],"domain_scores_gemma":[0.9991345,0.0002231936,0.0000830892,0.0003073709,0.0001266628,0.0001251359],"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":[0.0004136736,0.0003852266,0.01743471,0.00001186207,0.0001427092,0.0001453354,0.003203641,0.2940688,0.0004954446,0.004240282,0.005412246,0.674046],"study_design_scores_gemma":[0.0006397699,0.0000362993,0.009797052,0.00004592428,0.00001455421,0.00005097653,0.0000677577,0.9883556,0.00006948982,0.000730629,0.00002440409,0.0001675802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2143221,0.0001300322,0.7764779,0.0002975354,0.0002686732,0.0002550904,0.000001565771,0.0001037525,0.008143338],"genre_scores_gemma":[0.9823183,0.00002938062,0.01659239,0.0005010607,0.0002608443,0.000005958508,0.000002939614,0.00001433037,0.0002748233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7679961,"threshold_uncertainty_score":0.5538825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03766648286413913,"score_gpt":0.2591625106725789,"score_spread":0.2214960278084398,"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."}}