{"id":"W2547056635","doi":"10.1109/ccece.2016.7726607","title":"Selective decode-and-forward two-way relay network with weighted decision-feedback differential coherent detectors","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":"McGill University","funders":"","keywords":"Relay; Differential (mechanical device); Fading; Detector; Computer science; Differential coding; Modulation (music); SIGNAL (programming language); Phase-shift keying; Relay channel; Signal-to-noise ratio (imaging); Electronic engineering; Decoding methods; Topology (electrical circuits); Computer network; Algorithm; Bit error rate; Telecommunications; Physics; Engineering; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005448078,0.0007454044,0.0007419172,0.0003239716,0.0004165864,0.0006375037,0.0009656206,0.0007562584,0.0004733108],"category_scores_gemma":[0.0008748206,0.00033463,0.0004144736,0.0004239674,0.0006115763,0.001306549,0.0006978675,0.0003038986,0.0001702192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006619526,"about_ca_system_score_gemma":0.0006481181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933445,"about_ca_topic_score_gemma":0.002632396,"domain_scores_codex":[0.9994411,0.0002269191,0.00002505845,0.0001107201,0.0001307668,0.00006546167],"domain_scores_gemma":[0.9994653,0.0002992101,0.00008463878,0.00005530868,0.00007562901,0.00001995483],"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":[0.0004233073,0.00009855202,0.001179941,0.0002242299,0.0001512749,0.001176503,0.0002165794,0.8682684,0.03976212,0.04331872,0.000530182,0.0446501],"study_design_scores_gemma":[0.00004017385,0.0001883822,0.0001521238,0.000006719323,0.00004459241,0.0002729741,0.00002567432,0.9859033,0.007272908,0.005367142,0.0007073022,0.00001874733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1638085,0.0005913223,0.8318391,0.0001291027,0.00002689708,0.00009233205,0.00006866105,0.0001722917,0.00327184],"genre_scores_gemma":[0.9397264,0.000387003,0.05801559,0.0000414074,0.00001599976,0.00008990923,0.00003812192,0.000007117057,0.001678573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001933445,"threshold_uncertainty_score":0.004802823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127327547787016,"score_gpt":0.2434694857425959,"score_spread":0.2307367309638942,"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."}}