{"id":"W2120504056","doi":"10.1109/glocom.2006.544","title":"SPC03-1: On Decoding, Mutual Information, and Antenna Selection Diversity for Quasi-Orthogonal STBC with Minimum Decoding Complexity","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Computer science; Block code; Code (set theory); Algorithm; List decoding; Space–time block code; Selection (genetic algorithm); Theoretical computer science; Artificial intelligence; Concatenated error correction code","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":[0.0001077256,0.0001516719,0.0001540831,0.0001143527,0.0003509608,0.00004827189,0.0001490757,0.00006382258,0.00001108413],"category_scores_gemma":[0.00001769849,0.0001565815,0.00003123936,0.0001409573,0.00006398515,0.0005197907,0.00008817355,0.0001271256,0.000005891178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298905,"about_ca_system_score_gemma":0.00001047479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008648583,"about_ca_topic_score_gemma":0.0005567762,"domain_scores_codex":[0.9993263,0.00001521237,0.0002174518,0.0001156886,0.0001242558,0.0002010386],"domain_scores_gemma":[0.9995247,0.00008180481,0.00007716748,0.0001732945,0.00009657044,0.00004642517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001299603,0.0007204395,0.4445733,0.001026304,0.0003743298,0.000006612459,0.00289337,0.03082207,0.003737095,0.2737103,0.03661358,0.204223],"study_design_scores_gemma":[0.003376352,0.0008500883,0.1685679,0.0002315862,0.00007079051,0.00006344189,0.0004804763,0.7698685,0.0117238,0.01562803,0.02770852,0.001430562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4619057,0.00004176812,0.5360997,0.00005518763,0.00004283297,0.0002633653,0.00005962282,0.0005432867,0.0009884667],"genre_scores_gemma":[0.943211,0.000042515,0.05648932,0.0000583224,0.00003419214,0.000025478,0.0001103775,0.00001369533,0.00001504966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7390465,"threshold_uncertainty_score":0.6385209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01681689202823525,"score_gpt":0.2301798633270869,"score_spread":0.2133629712988516,"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."}}