{"id":"W4285120405","doi":"10.1109/tvt.2022.3186871","title":"Effective Capacity Analysis of AmBC-NOMA Communication Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Henan Provincial Science and Technology Research Project","keywords":"Noma; Computer science; Channel capacity; Electronic engineering; Computer network; Engineering; Telecommunications link","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":[0.0001761627,0.0001734455,0.0004231715,0.001928883,0.0003104146,0.000007490215,0.0008154858,0.0002005037,0.00004233474],"category_scores_gemma":[0.00000967468,0.0002048824,0.0001655405,0.003638618,0.0002635044,0.000078608,0.00001525754,0.000892583,0.000007791876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003264762,"about_ca_system_score_gemma":0.000008546365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004255357,"about_ca_topic_score_gemma":0.00003920035,"domain_scores_codex":[0.998913,0.0001352611,0.0003519624,0.0002063504,0.0001948962,0.0001984958],"domain_scores_gemma":[0.9980159,0.0001593299,0.0001152316,0.001607564,0.00008054555,0.00002138758],"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":[0.000007074103,0.00009714613,0.00005446131,0.0000161177,0.0008170505,0.000001243955,0.00008202167,0.9704893,0.0127505,0.003049807,0.00001239554,0.01262289],"study_design_scores_gemma":[0.0004976272,0.0002467419,0.0004162358,0.00002743415,0.0005978305,0.00002432055,0.001899331,0.7452366,0.246862,0.0009317711,0.002828612,0.0004315465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4438786,0.0008126191,0.5528938,0.0001174402,0.0001175969,0.0003608889,0.00008602798,0.00160214,0.0001308934],"genre_scores_gemma":[0.9965122,0.0002810432,0.001970583,0.00000785849,0.000001345598,0.001167146,0.00001394267,0.00003089197,0.00001502732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5526336,"threshold_uncertainty_score":0.8354861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009133841144506048,"score_gpt":0.2154872387590127,"score_spread":0.2063533976145067,"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."}}