{"id":"W4255746332","doi":"10.22215/etd/2016-11714","title":"Code Design for Incremental Redundancy Hybrid ARQ","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Hybrid automatic repeat request; Puncturing; Low-density parity-check code; Computer science; Turbo code; Forward error correction; Algorithm; Code rate; Error detection and correction; Automatic repeat request; Decoding methods; Computer network; Telecommunications; Telecommunications link","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000498892,0.0003099295,0.0002913291,0.0002117368,0.0001793041,0.0001547964,0.00145109,0.000152652,0.0000396245],"category_scores_gemma":[0.0001575286,0.0002572048,0.0001536766,0.0001197483,0.00001517555,0.0003412257,0.0000928051,0.0001621432,0.00006269599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156567,"about_ca_system_score_gemma":0.0002432644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000446506,"about_ca_topic_score_gemma":0.00009535975,"domain_scores_codex":[0.9981894,0.00006588089,0.0003662997,0.0006981439,0.0003123491,0.0003678783],"domain_scores_gemma":[0.9984075,0.0002901721,0.0002793139,0.0007318722,0.0002152716,0.00007582046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002886483,0.0002547188,0.00007421865,0.0004409956,0.000191863,0.00003984605,0.001599466,0.000003294206,0.08565721,0.06797908,0.4228784,0.4205922],"study_design_scores_gemma":[0.0003835368,0.000394564,0.00005875818,0.0004588962,0.00002786373,0.00002315086,0.00005311822,0.003616008,0.9465501,0.04030312,0.007336642,0.0007941847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009595707,0.00009120181,0.9712946,0.0001750751,0.00184828,0.001019937,0.00001443953,0.001545138,0.02305179],"genre_scores_gemma":[0.0778391,0.00004084628,0.8557643,0.0002376895,0.0002473002,0.0006584098,0.0001656038,0.0000943319,0.0649524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.860893,"threshold_uncertainty_score":0.999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03601431453519498,"score_gpt":0.3123210348762377,"score_spread":0.2763067203410427,"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."}}