{"id":"W2151095309","doi":"10.1109/icc.1988.13641","title":"Soft-decision decoding applied to the generalized type-II hybrid ARQ scheme","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Hybrid automatic repeat request; Decoding methods; Computer science; Algorithm; Automatic repeat request; Additive white Gaussian noise; Code (set theory); Scheme (mathematics); Theoretical computer science; Channel (broadcasting); Error detection and correction; Mathematics; Telecommunications; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007785329,0.0005219638,0.0005519097,0.0003360419,0.0003534115,0.000707962,0.0004700753,0.0005058465,0.001962925],"category_scores_gemma":[0.002696255,0.0001468104,0.0003576218,0.0005214407,0.0006111542,0.0004887002,0.0004371541,0.0005378103,0.0002892213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007099166,"about_ca_system_score_gemma":0.0006769091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004059081,"about_ca_topic_score_gemma":0.002909554,"domain_scores_codex":[0.9993161,0.0003477257,0.00002250489,0.00003674595,0.000213259,0.00006363207],"domain_scores_gemma":[0.9981996,0.001159417,0.0000955447,0.0002127054,0.0002948862,0.00003787227],"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":[0.0001691392,0.00002264212,0.0004392955,0.00005684373,0.0000353007,0.0001694262,0.0000848387,0.9462079,0.005352068,0.02587211,0.0004961979,0.0210942],"study_design_scores_gemma":[0.00002025243,0.00004554424,0.00008126435,0.000004081825,0.00000676096,0.00003615654,0.000008372023,0.9939481,0.002780657,0.002773372,0.0002871285,0.000008278314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.234093,0.000465395,0.7487592,0.0004748731,0.0002084899,0.0001548513,0.0001428804,0.0007524628,0.01494881],"genre_scores_gemma":[0.941156,0.0001720122,0.0566055,0.00005653872,0.00001605203,0.00004036281,0.0000327727,0.00002962871,0.001891145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004059081,"threshold_uncertainty_score":0.008070886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782250934438779,"score_gpt":0.2623441388897041,"score_spread":0.2445216295453164,"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."}}