{"id":"W2152904689","doi":"10.1109/spawc.2005.1506241","title":"Stochastic learning algorithms for adaptive modulation","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Link adaptation; Computer science; Modulation (music); Adaptive coding; Coding (social sciences); Wireless; Algorithm; Stochastic approximation; Mathematics; Fading; Decoding methods; Telecommunications","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":[0.002277391,0.001173137,0.001148485,0.0007916873,0.0005097392,0.001293116,0.001083025,0.001456002,0.003818182],"category_scores_gemma":[0.01048757,0.0004187436,0.0006502517,0.001079303,0.001434449,0.001589772,0.001529481,0.002679455,0.001035991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320768,"about_ca_system_score_gemma":0.001268023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00290141,"about_ca_topic_score_gemma":0.00191462,"domain_scores_codex":[0.9989663,0.0004602244,0.00004947411,0.0001444364,0.0002825928,0.0000970273],"domain_scores_gemma":[0.9959556,0.003103743,0.0002257112,0.0001606752,0.0004610643,0.00009335421],"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":[0.00004065603,0.00003623378,0.000278515,0.0001062157,0.00004507045,0.00002870806,0.00004978132,0.6708534,0.000497182,0.2660654,0.003229681,0.05876916],"study_design_scores_gemma":[0.000009465495,0.00001246214,0.00003838329,0.000009727619,0.000003568626,0.00000775907,0.00000371419,0.9264178,0.000109691,0.07199316,0.001388632,0.000005623422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001563428,0.001016779,0.9943526,0.0003363843,0.00007776417,0.00002251749,0.0000293329,0.0000831913,0.002518112],"genre_scores_gemma":[0.4408968,0.006811298,0.5271258,0.0008437623,0.001116986,0.0007946755,0.000556205,0.0002641966,0.02159028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003818182,"threshold_uncertainty_score":0.0127731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451742492764197,"score_gpt":0.2323157236794982,"score_spread":0.2177982987518563,"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."}}