{"id":"W4415293221","doi":"10.1109/mwc.2025.3596934","title":"SpectrumLLM: Large Language Models for Next-Generation Spectrum Prediction","year":2025,"lang":"","type":"article","venue":"IEEE Wireless Communications","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Wireless; Generalization; Adaptability; Field (mathematics); Interference (communication); Spectrum management; Wireless network; Spectrum (functional analysis)","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":[0.0006403224,0.0004118141,0.0004249669,0.0004007998,0.002138782,0.00091299,0.003348567,0.0002149709,0.0000145838],"category_scores_gemma":[0.00002621663,0.000508294,0.000331544,0.001735494,0.0002024668,0.001243124,0.0007709759,0.0005075481,0.00006703658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003057504,"about_ca_system_score_gemma":0.0007277473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001300405,"about_ca_topic_score_gemma":0.0004982661,"domain_scores_codex":[0.9969124,0.0002784171,0.0009540315,0.0008700538,0.0003713651,0.0006136968],"domain_scores_gemma":[0.9937926,0.0006409962,0.0003899472,0.004572106,0.0004563353,0.00014801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001075245,0.0008217607,0.00002362369,0.0000538854,0.0001357584,2.872782e-7,0.00175764,0.01231262,0.007642399,0.9431138,0.007125531,0.02700196],"study_design_scores_gemma":[0.0007614535,0.00005217495,0.0001876086,0.00009701977,0.00009797724,0.000002432961,0.000159535,0.8640034,0.002660096,0.1217347,0.009912058,0.0003316035],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005133265,0.002082704,0.9610868,0.02095506,0.001353436,0.001824185,0.0008175682,0.0002994457,0.006447609],"genre_scores_gemma":[0.9704705,0.001172886,0.02331381,0.00100865,0.000538921,0.0009947106,0.0008758807,0.00004248636,0.001582172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9653372,"threshold_uncertainty_score":0.9997368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05599980798980813,"score_gpt":0.3093203023341852,"score_spread":0.2533204943443771,"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."}}