{"id":"W2054898736","doi":"10.1155/asp/2006/35352","title":"Spectrally Efficient Communication over Time-Varying Frequency-Selective Mobile Channels: Variable-Size Burst Construction and Adaptive Modulation","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Link adaptation; Spectral efficiency; Channel (broadcasting); Quality of service; Modulation (music); Key (lock); Fading; Variable (mathematics); Burst mode (computing); Real-time computing; Adaptation (eye); Electronic engineering; Telecommunications; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001110937,0.0002316171,0.0002649364,0.0003268156,0.0007385237,0.0005881254,0.0008138797,0.00009191175,0.00002192848],"category_scores_gemma":[0.00007419937,0.0002244227,0.00004182501,0.001166321,0.0002139007,0.002220125,0.0001750594,0.0009719082,0.000005744146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004442895,"about_ca_system_score_gemma":0.0001852081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001642989,"about_ca_topic_score_gemma":0.000004712455,"domain_scores_codex":[0.9974568,0.0004854685,0.0005878164,0.000409553,0.000639441,0.0004208634],"domain_scores_gemma":[0.9979416,0.0006977229,0.0005418751,0.0003509732,0.0003819947,0.00008586423],"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":[0.000111207,0.0001680035,0.0007145713,0.00002018408,0.000008976215,0.00001101464,0.000407684,0.8520188,0.003447179,0.006886032,0.00001714809,0.1361892],"study_design_scores_gemma":[0.0007974384,0.0002205026,0.00197599,0.0005995975,0.00000489695,0.0001825326,0.000067119,0.9601786,0.0009689133,0.03456602,0.0001518529,0.000286587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07185142,0.01114294,0.9101838,0.0003009819,0.00014973,0.0004879723,0.000001972192,0.0001234071,0.005757712],"genre_scores_gemma":[0.8810254,0.0004310478,0.1182657,0.00005547567,0.0001420601,0.00002958336,0.000003277962,0.00001927811,0.00002819428],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8091739,"threshold_uncertainty_score":0.9151691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132488380554448,"score_gpt":0.2727373087615287,"score_spread":0.2614124249559842,"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."}}