{"id":"W2168590314","doi":"10.1109/26.923811","title":"Performance of multidimensional multicode DS-CDMA using code diversity and error detection","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Diversity scheme; Diversity gain; Electronic engineering; Precoding; Fading; Multipath propagation; Code division multiple access; Channel (broadcasting); Algorithm; Telecommunications; MIMO; Engineering","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":[0.0001049064,0.0001407147,0.0001592098,0.0001935652,0.001018813,0.000005832228,0.0004030427,0.00008815808,0.00001372947],"category_scores_gemma":[0.000004732266,0.0001657547,0.000052689,0.0002806224,0.0002153609,0.0002816454,0.00003752723,0.0003403097,0.000004293855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128918,"about_ca_system_score_gemma":0.000009845891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008935543,"about_ca_topic_score_gemma":0.0002617431,"domain_scores_codex":[0.9992754,0.00006462749,0.0002687231,0.000122375,0.0001300452,0.0001388184],"domain_scores_gemma":[0.9984089,0.0001937549,0.00006600869,0.001167334,0.000105787,0.00005821347],"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":[0.0000941689,0.0005227627,0.001586014,0.00008514241,0.0001398911,6.739424e-7,0.001913815,0.6838956,0.2223347,0.0001335065,0.000008390299,0.08928531],"study_design_scores_gemma":[0.0002903495,0.00004086735,0.001589392,0.000062691,0.00003743518,0.00001643474,0.0001092855,0.8981722,0.09909161,0.00002979317,0.0003920385,0.0001679149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5710483,0.0001149146,0.4281397,0.00003813403,0.0000519042,0.0001504866,0.00002290718,0.0002798577,0.0001538231],"genre_scores_gemma":[0.9490086,0.003243829,0.04764255,0.00001333286,0.000003967038,0.00003345965,0.000003649504,0.00002560207,0.00002496779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3804971,"threshold_uncertainty_score":0.7835988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05725839522726844,"score_gpt":0.2844014204847314,"score_spread":0.227143025257463,"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."}}