{"id":"W4200621736","doi":"10.36227/techrxiv.16653001.v1","title":"Optimum Elements Selection in IRS-Assisted UAV Communications with Phase Errors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Estimator; Selection (genetic algorithm); Bit error rate; Algorithm; Reduction (mathematics); Computer science; Phase (matter); Channel (broadcasting); Mathematical optimization; Mathematics; Telecommunications; Statistics; Artificial intelligence; Physics","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":[0.000123904,0.0003205043,0.0003766867,0.0003700874,0.00009265468,0.00007348409,0.001375307,0.0003327372,0.0000757625],"category_scores_gemma":[0.00003686799,0.0003368275,0.00005680626,0.0006478848,0.0001112794,0.0001778338,0.001317554,0.001452143,0.000008239031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000526312,"about_ca_system_score_gemma":0.00008298995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006753733,"about_ca_topic_score_gemma":0.002037504,"domain_scores_codex":[0.9986314,0.00007723597,0.0005363488,0.0003085452,0.0001708919,0.000275543],"domain_scores_gemma":[0.9973662,0.00008606307,0.0001371982,0.00224173,0.0001274367,0.00004139751],"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":[0.00003971287,0.00122408,0.003686727,0.0004875511,0.0005251928,0.00001363591,0.0008556459,0.8183316,0.01185607,0.001583297,0.0005999887,0.1607965],"study_design_scores_gemma":[0.002477944,0.00009561938,0.003029405,0.0008176185,0.00005526239,0.00001952038,0.003745256,0.9652093,0.01861997,0.000619042,0.004063225,0.001247843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3558799,0.003501367,0.6150246,0.001181323,0.0002254856,0.001622725,0.00004727299,0.007000796,0.01551661],"genre_scores_gemma":[0.7593931,0.002214831,0.2374227,0.00001715917,0.000004890388,0.0004756453,0.0003282547,0.00005711815,0.00008631442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4035132,"threshold_uncertainty_score":0.9999084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04072883071061742,"score_gpt":0.3181098810198266,"score_spread":0.2773810503092091,"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."}}