{"id":"W4414871454","doi":"10.1109/tnse.2025.3617102","title":"RadioMamba: Breaking the Accuracy-Efficiency Trade-Off in Radio Map Construction via a Hybrid Mamba-UNet","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Block (permutation group theory); Context (archaeology); Channel (broadcasting); Wireless; Architecture; Feature (linguistics); Global optimization","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":[0.0004534952,0.0006602542,0.0005623734,0.000342161,0.0003168209,0.0007155532,0.001632051,0.0006882108,0.002809397],"category_scores_gemma":[0.001420393,0.0003611141,0.0004039095,0.0003104717,0.0004892486,0.001236972,0.001392062,0.001221969,0.00114234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004956612,"about_ca_system_score_gemma":0.0009154135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004282186,"about_ca_topic_score_gemma":0.008236339,"domain_scores_codex":[0.9998263,0.00003067386,0.000007761575,0.00004643774,0.00005925373,0.00002965797],"domain_scores_gemma":[0.9997185,0.00008302654,0.00002635215,0.0000737658,0.00007390903,0.0000244942],"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":[0.0002967643,0.0001255988,0.001670041,0.0001514998,0.0001146394,0.0001380511,0.00008628629,0.5103886,0.03400814,0.01724393,0.007785049,0.4279914],"study_design_scores_gemma":[0.000007081963,0.00003332719,0.0001280799,0.00000503055,0.000008096183,0.00003034024,0.00000576779,0.9919516,0.003871372,0.001996927,0.001956774,0.000005598278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02298636,0.000406538,0.9692653,0.0002989835,0.00009165867,0.00004357677,0.0001255279,0.003164269,0.003617838],"genre_scores_gemma":[0.5428732,0.0003321928,0.4468883,0.0004783898,0.00005812104,0.000176445,0.0006228476,0.0003541112,0.008216335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004282186,"threshold_uncertainty_score":0.009398401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004994350136934099,"score_gpt":0.2012223092473511,"score_spread":0.196227959110417,"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."}}