{"id":"W2094148320","doi":"10.1002/ett.1010","title":"Analysis of coherent MPSK schemes with generalized selection diversity in Rayleigh fading","year":2004,"lang":"en","type":"article","venue":"European Transactions on Telecommunications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Medical Research Council; King Fahd University of Petroleum and Minerals","keywords":"Rayleigh fading; Expression (computer science); Phase-shift keying; Fading; Keying; Moment-generating function; Selection (genetic algorithm); Diversity combining; Modulation (music); Algorithm; Computer science; Mathematics; Diversity scheme; Rayleigh scattering; Electronic engineering; Probability density function; Telecommunications; Statistics; Bit error rate; Physics; Engineering; Optics; Artificial intelligence; Decoding methods; Acoustics","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":[0.0001797686,0.0001638759,0.0002568692,0.0008325122,0.0003156104,0.00001578175,0.0005874909,0.00004022145,0.00006578994],"category_scores_gemma":[0.000006407113,0.0001763233,0.0001115018,0.002117563,0.00008766856,0.0001969577,0.0000265286,0.0004014548,0.000009468151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002619324,"about_ca_system_score_gemma":0.00001705746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001302362,"about_ca_topic_score_gemma":0.001321611,"domain_scores_codex":[0.9990203,0.0001768977,0.0003559635,0.0001611593,0.0001208797,0.0001647398],"domain_scores_gemma":[0.9987624,0.00008360788,0.00008369557,0.000936155,0.00008410484,0.00005004028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001605415,0.0002412739,0.001234953,0.00001051014,0.0003938159,5.467113e-7,0.0007542688,0.9865661,0.002901872,0.001064028,0.00000375548,0.006812807],"study_design_scores_gemma":[0.01112785,0.001026677,0.2350344,0.001045136,0.003956186,0.00002966093,0.002324708,0.2342988,0.4967608,0.001615084,0.008904437,0.003876253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2607837,0.0001339843,0.734013,0.00008406501,0.00001336696,0.0002243002,0.00002171219,0.0005545965,0.004171219],"genre_scores_gemma":[0.942257,0.001075099,0.05650217,0.00001973056,0.000002764887,0.00003794934,0.000038228,0.00003780018,0.00002930183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7522673,"threshold_uncertainty_score":0.7190256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162674496828469,"score_gpt":0.2439785724252331,"score_spread":0.2223518274569484,"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."}}