{"id":"W2519970793","doi":"10.1109/wcnc.2016.7564959","title":"End-to-end distortion analysis of multicasting over orthogonal receive component decode-forward cooperative broadcast channels","year":2016,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Channel (broadcasting); Computer network; Decodes; Distortion (music); Relay; Decoding methods; Broadcasting (networking); Multicast; Layer (electronics); Transmission (telecommunications); Telecommunications; Bandwidth (computing)","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":[0.001561047,0.001128098,0.0008524981,0.0005859919,0.0003816101,0.001010267,0.001083271,0.0007046708,0.001050318],"category_scores_gemma":[0.006161448,0.0003372064,0.0004283115,0.0004427751,0.001119314,0.001090975,0.0009384616,0.0008748898,0.0002607345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002441632,"about_ca_system_score_gemma":0.0007629029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321801,"about_ca_topic_score_gemma":0.0018002,"domain_scores_codex":[0.9986042,0.000383623,0.00003913276,0.0001330861,0.0006613021,0.0001786241],"domain_scores_gemma":[0.9946478,0.00351022,0.000573996,0.0002693417,0.0008867709,0.0001117987],"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":[0.0003350781,0.00004417279,0.0010814,0.0001296238,0.00006405161,0.0002766435,0.0001660355,0.9425409,0.0131946,0.0261519,0.0004668541,0.01554876],"study_design_scores_gemma":[0.000003943996,0.00006953022,0.0002842532,0.000005919697,0.00001480912,0.0001193881,0.00002559289,0.9919063,0.004518873,0.002816461,0.0002227018,0.00001211866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1290355,0.001561201,0.8607729,0.0002549478,0.00004446676,0.00004494127,0.0001515143,0.0002000467,0.007934471],"genre_scores_gemma":[0.9728142,0.0007456039,0.02350137,0.00004174702,0.00003551271,0.0000382196,0.0001028009,0.00005477963,0.002665919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003321801,"threshold_uncertainty_score":0.01771533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942130668605934,"score_gpt":0.2983598156605762,"score_spread":0.2589385089745169,"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."}}