{"id":"W2892912714","doi":"10.1109/tit.2018.2872053","title":"A Joint Typicality Approach to Compute–Forward","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Ministerio de Economía y Competitividad; National Science Foundation","keywords":"Decoding methods; Computer science; Encoding (memory); Gaussian; Joint (building); Theoretical computer science; Algorithm; Context (archaeology); Channel (broadcasting); Mathematics; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006895176,0.0001229825,0.0001247023,0.00023323,0.0005077046,0.000187627,0.0006392752,0.00005378474,0.00008886457],"category_scores_gemma":[0.0000137522,0.0001120509,0.00007324821,0.0006279756,0.00007749556,0.001455575,0.00001288096,0.0001929315,0.001375904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000733393,"about_ca_system_score_gemma":0.00004920068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001571758,"about_ca_topic_score_gemma":0.000001652106,"domain_scores_codex":[0.998936,0.0001861303,0.0003364887,0.0001482198,0.0002096686,0.0001835107],"domain_scores_gemma":[0.9987193,0.00008143313,0.00007130256,0.0007418561,0.000254574,0.0001315048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005015497,0.0001504683,4.524815e-7,0.00001049112,0.00002943777,7.372667e-8,0.006801325,0.005807383,0.0001177491,0.4904441,0.002520504,0.4940678],"study_design_scores_gemma":[0.001604448,0.000746841,0.0005806225,0.0001081215,0.00002611138,0.00003866269,0.0006306788,0.7679167,0.03978549,0.01477681,0.1727173,0.001068233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005351907,0.000004983461,0.9690327,0.0007174895,0.000454471,0.0002491408,0.000004583256,0.0002783084,0.02872315],"genre_scores_gemma":[0.9484001,0.00001614724,0.04545903,0.00578418,0.00004440591,0.00005845894,0.000002877297,0.000004747699,0.000230028],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9478649,"threshold_uncertainty_score":0.9994016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03505041804431539,"score_gpt":0.26141645457965,"score_spread":0.2263660365353346,"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."}}