{"id":"W2104322921","doi":"10.1109/lcomm.2011.071311.111110","title":"Channel Coding Diversity with Mismatched Decoding Metrics","year":2011,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Decoding methods; Computer science; Baud; Diversity scheme; Algorithm; Channel (broadcasting); Coding (social sciences); Antenna diversity; Diversity gain; Telecommunications; Bit error rate; Theoretical computer science; Electronic engineering; Mathematics; Fading; Wireless; Statistics; 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":[0.0001639545,0.0001631439,0.0001756139,0.0002757242,0.0004532031,0.00003236338,0.002286198,0.00008406022,0.00001382412],"category_scores_gemma":[0.0000448796,0.0001655643,0.0000478273,0.0006930613,0.0002946277,0.0002432425,0.0007538248,0.0004180846,0.00005735544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266755,"about_ca_system_score_gemma":0.000006240324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004954414,"about_ca_topic_score_gemma":0.00003946607,"domain_scores_codex":[0.999212,0.00004975797,0.0002095342,0.0001327166,0.000141912,0.0002540545],"domain_scores_gemma":[0.997182,0.0002329675,0.00005683459,0.002404141,0.00006076405,0.00006325435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000324867,0.003546704,0.09888701,0.001340606,0.005904765,0.0001582395,0.1033534,0.04806751,0.2872426,0.1920723,0.06594729,0.1931546],"study_design_scores_gemma":[0.004591662,0.0003090869,0.02772458,0.0007933787,0.0005665859,0.0001098162,0.01165586,0.5601236,0.3715165,0.00424678,0.01276593,0.005596182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6137415,0.00152365,0.3459052,0.006398492,0.0003493112,0.0006126001,0.0000213537,0.005503924,0.02594389],"genre_scores_gemma":[0.9380064,0.001014953,0.06061802,0.00026797,0.000006686368,0.00004112363,0.000007520767,0.00003072657,0.000006580623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5120561,"threshold_uncertainty_score":0.6751515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08239428994472643,"score_gpt":0.2316721656180303,"score_spread":0.1492778756733039,"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."}}