{"id":"W50682970","doi":"10.1007/978-1-4757-3569-7_19","title":"A Downlink SS-OFDM-F/TA Packet Data System Employing Multi-User Diversity","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Orthogonal frequency-division multiplexing; Computer science; Telecommunications link; Hybrid automatic repeat request; Computer network; Link adaptation; Network packet; Automatic repeat request; Frequency-division multiplexing; Throughput; Time-division multiplexing; Multiplexing; Scheduling (production processes); Electronic engineering; Real-time computing; Telecommunications; Fading; Wireless; Engineering; 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"],"consensus_categories":[],"category_scores_codex":[0.0001648287,0.0006099339,0.0005934951,0.0001771751,0.0002295667,0.00006091582,0.0009371631,0.0005785447,0.000594034],"category_scores_gemma":[0.00001255863,0.0006513096,0.0001043283,0.00006537824,0.00004734969,0.0004683418,0.001433242,0.0005839592,0.0006690627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004176997,"about_ca_system_score_gemma":0.00001221904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001473984,"about_ca_topic_score_gemma":0.00006341525,"domain_scores_codex":[0.9979244,0.00001678466,0.0004578464,0.0007062693,0.000468698,0.00042598],"domain_scores_gemma":[0.9977744,0.00006645583,0.0001380597,0.001743414,0.0001198199,0.0001579028],"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":[0.00001125135,0.00002376806,0.0001817467,0.0008291941,0.0006168987,0.0001328737,0.0001812087,0.9131855,0.000005908532,0.02526215,0.05182907,0.007740478],"study_design_scores_gemma":[0.0006789131,0.00001014476,0.00001625352,0.0006601787,0.0002575335,0.00001823869,0.00002950894,0.8377556,0.000005410122,0.00005518577,0.1594722,0.001040814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001591858,0.001453891,0.5021534,0.00001516269,0.001765213,0.0007537141,0.0006838012,0.003382976,0.4897759],"genre_scores_gemma":[0.01274413,0.003014238,0.06005477,0.00006945685,0.001119446,0.00001609012,0.002672259,0.0006751673,0.9196345],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4420986,"threshold_uncertainty_score":0.9995938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0648101858910365,"score_gpt":0.225659562406077,"score_spread":0.1608493765150405,"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."}}