{"id":"W2115167160","doi":"10.1109/ccece.2008.4564684","title":"Adaptive coded cooperative relaying schemes","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Signal-to-noise ratio (imaging); Outage probability; Computer science; Range (aeronautics); Probability of error; Algorithm; Mathematical optimization; Decoding methods; Fading; Mathematics; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005402546,0.0007667803,0.0004377872,0.0006920897,0.0004997177,0.0008939851,0.001478383,0.000941072,0.001695897],"category_scores_gemma":[0.002333708,0.0001553383,0.0002796313,0.001008099,0.0005733196,0.0007874674,0.0009176303,0.0005529449,0.0003849769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006791959,"about_ca_system_score_gemma":0.0009526645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002770996,"about_ca_topic_score_gemma":0.004131576,"domain_scores_codex":[0.9993611,0.0001232679,0.00003875795,0.0001109014,0.000246907,0.0001190194],"domain_scores_gemma":[0.9984793,0.0005257003,0.0001740178,0.0001961849,0.0005739566,0.000050895],"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":[0.0005754053,0.000125291,0.001556695,0.0004265439,0.0001203678,0.0008628314,0.0004820446,0.4376788,0.06361802,0.1119349,0.005819912,0.3767992],"study_design_scores_gemma":[0.0000736949,0.0002637825,0.0005267639,0.00004176834,0.00006537548,0.0007066386,0.00006609617,0.9563808,0.01294317,0.02215375,0.006698332,0.00007972134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07391068,0.001575003,0.9051759,0.0003022021,0.000164954,0.000253447,0.0002117332,0.0004295478,0.0179766],"genre_scores_gemma":[0.8991048,0.0007255168,0.09420261,0.0001626704,0.00005314637,0.0001948758,0.0001027016,0.00001116571,0.005442627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002770996,"threshold_uncertainty_score":0.005673349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03909456787277747,"score_gpt":0.2185647357009534,"score_spread":0.179470167828176,"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."}}