{"id":"W2120796513","doi":"10.1109/icc.2006.255015","title":"Performance of Selection Diversity MFSK in the Presence of Estimation Errors","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Selection (genetic algorithm); Noise (video); Diversity combining; Independent and identically distributed random variables; Algorithm; Computer science; Signal-to-noise ratio (imaging); A priori and a posteriori; Statistics; Frequency-shift keying; Mathematics; Noise power; SIGNAL (programming language); Keying; Power (physics); Speech recognition; Telecommunications; Fading; Demodulation; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002149987,0.00008779292,0.0001069668,0.000185443,0.00009698183,0.00001035518,0.001515108,0.00004838478,0.00002309767],"category_scores_gemma":[0.00002998594,0.00008422723,0.00003289617,0.0002818546,0.0001526226,0.0002412707,0.0001329992,0.0002237455,0.000004706486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000757755,"about_ca_system_score_gemma":0.00002016585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003935743,"about_ca_topic_score_gemma":0.0005069375,"domain_scores_codex":[0.999178,0.00008259899,0.0003353479,0.00007716886,0.000245427,0.00008143561],"domain_scores_gemma":[0.9987155,0.0001970117,0.0001436582,0.0007074012,0.0002272277,0.000009153707],"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":[0.00005606061,0.000726836,0.05368533,0.00008664386,0.00004815194,2.917059e-7,0.001601786,0.6397132,0.02573391,0.258242,0.001553208,0.01855252],"study_design_scores_gemma":[0.0001499276,0.00003994442,0.03593501,0.0001190121,0.000005979291,0.000001785828,0.0001143286,0.942104,0.01822425,0.002975248,0.000228454,0.0001020513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148065,0.00007209568,0.02547247,0.0008172885,0.000109127,0.0003604533,0.000054808,0.0001587191,0.05814856],"genre_scores_gemma":[0.9899647,0.0005081636,0.009354852,0.00001236853,0.000007871154,0.00005010594,0.00005106497,0.000007195816,0.00004368601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3023908,"threshold_uncertainty_score":0.3434687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0546348964081913,"score_gpt":0.3105612136936934,"score_spread":0.2559263172855021,"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."}}