{"id":"W2132211658","doi":"10.1109/twc.2009.081108","title":"Hard/soft detection with limited CSI for multi-hop systems","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Channel state information; Overhead (engineering); Relay; Channel (broadcasting); Diversity gain; Maximum a posteriori estimation; Algorithm; Computer network; Wireless; Telecommunications; Maximum likelihood; MIMO; Mathematics; Power (physics); Statistics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003050929,0.0002486042,0.0002601189,0.00027916,0.001553223,0.0003056962,0.002230218,0.0001106794,0.000003702492],"category_scores_gemma":[0.00001077516,0.0002361605,0.0001254037,0.0009849387,0.000114659,0.000569271,0.00001277675,0.0004615012,0.00002766299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000147206,"about_ca_system_score_gemma":0.00008727787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000016456,"about_ca_topic_score_gemma":0.0002888045,"domain_scores_codex":[0.9983937,0.0003008746,0.0003978965,0.0003888398,0.000210441,0.0003082809],"domain_scores_gemma":[0.9954059,0.0004378946,0.0001625751,0.003360616,0.0004986592,0.0001343641],"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":[0.0001062461,0.00185715,0.00001066563,0.00002529555,0.0001575572,0.000001068419,0.001791802,0.03106996,0.02963217,0.02671411,0.00020842,0.9084256],"study_design_scores_gemma":[0.001163812,0.0003884057,0.000389395,0.0001285175,0.00003733168,0.00001991463,0.0001014929,0.9826505,0.007564529,0.00004655858,0.007128119,0.0003813796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001970805,0.000432179,0.9930566,0.002542354,0.0002538751,0.0008753164,0.00001652928,0.0005939331,0.0002584384],"genre_scores_gemma":[0.941039,0.001243286,0.05615646,0.0004248793,0.00002090095,0.0005209649,0.00001136111,0.00002272618,0.0005603806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9515806,"threshold_uncertainty_score":0.9997466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0784405269543637,"score_gpt":0.2994393556323169,"score_spread":0.2209988286779532,"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."}}