{"id":"W7124855864","doi":"10.1109/ucom67224.2025.11336912","title":"From Survey to Design: Knowledge-Enhanced Multimodal Spectrum Foundation Model for Intelligent Spectrum Management","year":2025,"lang":"","type":"article","venue":"","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China","keywords":"Wireless; Spectrum management; Adaptability; Cognitive radio; Spectrum (functional analysis); Foundation (evidence); Wireless network; Channel (broadcasting)","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001869753,0.0006790897,0.0006315441,0.0009001525,0.0005498236,0.00103799,0.001920627,0.0002550318,0.0002238587],"category_scores_gemma":[0.0002013931,0.0007629466,0.0002402206,0.002124691,0.00008702648,0.0008057935,0.0008209763,0.0002360649,0.0009457821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385232,"about_ca_system_score_gemma":0.0005511453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005336059,"about_ca_topic_score_gemma":0.001628364,"domain_scores_codex":[0.9941469,0.0005597049,0.001466873,0.002300287,0.0005835831,0.0009425816],"domain_scores_gemma":[0.9958902,0.001136473,0.0003700455,0.001755741,0.0005255827,0.000321968],"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":[0.0005451564,0.000841689,0.0001077693,0.0001139623,0.0003075324,8.318142e-7,0.002507405,0.6573904,0.002037613,0.1236405,0.002813158,0.2096941],"study_design_scores_gemma":[0.0009617987,0.0001650195,0.01177981,0.0001848939,0.00007206612,1.249569e-7,0.00008737989,0.8810989,0.04435239,0.06035328,0.0002882492,0.0006561219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002288866,0.00008465494,0.9803946,0.005878934,0.003206045,0.005119439,0.00004636849,0.0002862312,0.002694844],"genre_scores_gemma":[0.7339682,0.00003371547,0.2496261,0.0003273466,0.0001947744,0.0004539034,0.0001174841,0.0000387999,0.01523965],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7316793,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07912505726096049,"score_gpt":0.3341934457118215,"score_spread":0.255068388450861,"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."}}