{"id":"W4411996945","doi":"10.1109/tmlcn.2025.3585849","title":"Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Proof of concept; Wireless; Computer science; Cognition; Cognitive radio; Spectrum (functional analysis); Human–computer interaction; Computer security; Psychology; Telecommunications; Neuroscience","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":[0.0007236176,0.0006001959,0.0003694179,0.0002613841,0.0002397026,0.0006176611,0.001229853,0.0008189674,0.001555181],"category_scores_gemma":[0.0006730186,0.0002000371,0.0003339053,0.0001479936,0.0004765057,0.0008397924,0.0004923712,0.000681841,0.000601003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000512299,"about_ca_system_score_gemma":0.0007822407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007878457,"about_ca_topic_score_gemma":0.0008062207,"domain_scores_codex":[0.9996119,0.00006989011,0.00001127778,0.00007938294,0.0001763845,0.0000511265],"domain_scores_gemma":[0.9997389,0.00005951556,0.00004524243,0.00003175968,0.000098392,0.00002611655],"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":[0.0004212275,0.0004997024,0.001672526,0.0005207394,0.0001459065,0.0006387102,0.0001917722,0.1876048,0.2524455,0.03953915,0.0100693,0.5062507],"study_design_scores_gemma":[0.00004534425,0.0005546205,0.000412034,0.0000176507,0.00001479132,0.0002462612,0.00001727906,0.9291501,0.05901807,0.001839444,0.00866113,0.00002318322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0167978,0.0002492199,0.9765793,0.0003781163,0.00008372528,0.0002332201,0.00005143051,0.001692509,0.003934672],"genre_scores_gemma":[0.4982279,0.0003035894,0.4956368,0.0002832765,0.00006548731,0.0004430419,0.0001472904,0.00008024308,0.004812258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001555181,"threshold_uncertainty_score":0.005202591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325191735293836,"score_gpt":0.278034530094821,"score_spread":0.2547826127418827,"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."}}