{"id":"W4411632506","doi":"10.1007/s11432-024-4426-0","title":"Large AI model for delay-Doppler domain channel prediction in 6G OTFS-based vehicular networks","year":2025,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Doppler effect; Time domain; Channel (broadcasting); Domain (mathematical analysis); Artificial intelligence; Computer network; Mathematics; Computer vision; Physics","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":[0.00250379,0.0001453789,0.000133119,0.0009839714,0.0006607398,0.0003177158,0.0005793355,0.00008040266,0.000008445148],"category_scores_gemma":[0.0001336199,0.0001328887,0.00004964563,0.002991721,0.0004598289,0.004246129,0.00004945271,0.0001668089,0.00001197816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002284905,"about_ca_system_score_gemma":0.0003298397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009095427,"about_ca_topic_score_gemma":0.00001420815,"domain_scores_codex":[0.998256,0.00001287248,0.0004538296,0.0002164453,0.0005289494,0.0005318875],"domain_scores_gemma":[0.999483,0.00003206571,0.00007309897,0.0002000594,0.0001320816,0.00007970593],"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":[0.000004003148,0.000009013859,0.0002807141,0.00002402036,0.000001405781,5.619446e-8,0.0008334217,0.9917042,0.0001022072,0.004628253,0.001910744,0.0005019623],"study_design_scores_gemma":[0.0004085702,0.00002174202,0.001809405,0.00004748767,0.000002568948,0.000001886285,0.0004036423,0.9921988,0.0005366846,0.003230011,0.001207617,0.0001315922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09187515,0.00004260866,0.9011093,0.0005312335,0.001243245,0.000453243,0.00002658347,0.0002727324,0.004445947],"genre_scores_gemma":[0.9961358,0.000008171712,0.003143222,0.0004675552,0.00003357821,0.0001551478,0.00001461903,0.000003921069,0.00003794989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9042607,"threshold_uncertainty_score":0.5419043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00932262461706957,"score_gpt":0.262640152346049,"score_spread":0.2533175277289795,"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."}}