{"id":"W2200746630","doi":"10.1109/lwc.2016.2516543","title":"Saddle Point Approximation for Outage Probability Using Cumulant Generating Functions","year":2016,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saddle point; Cumulant; Fading; Rician fading; Point (geometry); Saddle; Channel (broadcasting); Wireless","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":[0.0002623171,0.0001812215,0.0001937932,0.0001118895,0.0003553532,0.00004616563,0.0003786615,0.00007359489,0.000005246885],"category_scores_gemma":[0.00005267961,0.0001617929,0.00008118092,0.0001934994,0.00009667069,0.0004899639,0.00005104889,0.0001057047,0.00001405336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004007261,"about_ca_system_score_gemma":0.00001940024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160748,"about_ca_topic_score_gemma":0.00004426588,"domain_scores_codex":[0.9988815,0.00009150305,0.0004582148,0.0002147455,0.0000951242,0.0002589709],"domain_scores_gemma":[0.9981286,0.0002414687,0.0001208902,0.001336987,0.0001173019,0.00005474931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003331806,0.00002487977,0.0001049552,0.00005288273,0.00002661529,1.003806e-7,0.0002125028,0.4851224,0.5092565,0.0003698654,0.0002810205,0.004544871],"study_design_scores_gemma":[0.0004529868,0.000008777431,0.0000171122,0.0001209966,0.0000261369,0.00000693513,0.00007726553,0.9875472,0.01066772,0.0001168717,0.0006954183,0.0002625748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1536349,0.00006276956,0.843917,0.0007665817,0.0003002661,0.0007771009,0.00006978104,0.0004081336,0.0000634252],"genre_scores_gemma":[0.7539944,0.00002758447,0.2452154,0.00008365462,0.00009628084,0.0004323,0.00006427987,0.00005636122,0.00002972646],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6003595,"threshold_uncertainty_score":0.6597723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04789240507606581,"score_gpt":0.2627135766011653,"score_spread":0.2148211715250996,"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."}}