{"id":"W2622829522","doi":"10.1109/tcomm.2017.2712601","title":"TAS-Based Incremental Hybrid Decode–Amplify–Forward Relaying for Physical Layer Security Enhancement","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Australian Research Council; National Natural Science Foundation of China","keywords":"Relay; Physical layer; Computer science; Overhead (engineering); Transmission (telecommunications); Computer network; Channel state information; Secure transmission; Secrecy; Scheme (mathematics); Electronic engineering; Topology (electrical circuits); Wireless; Engineering; Telecommunications; Power (physics); Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004750948,0.0002285305,0.000242047,0.0001256171,0.004428854,0.0004661009,0.004474194,0.00005584188,0.00003764159],"category_scores_gemma":[0.00003341064,0.0002422058,0.00021413,0.0001502554,0.0002254675,0.0006391949,0.00008494503,0.0005059071,0.00008066559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821842,"about_ca_system_score_gemma":0.0001343199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002378287,"about_ca_topic_score_gemma":0.0002764924,"domain_scores_codex":[0.9984636,0.0002241561,0.0003482361,0.0003948906,0.0002504606,0.0003186782],"domain_scores_gemma":[0.9928269,0.000624575,0.0002377643,0.005946048,0.0002281568,0.000136583],"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":[0.000308205,0.009708096,0.0001308614,0.0001132676,0.0006917277,0.000004304768,0.006018183,0.008794803,0.03714748,0.1421865,0.009550378,0.7853462],"study_design_scores_gemma":[0.001620529,0.0003023793,0.0001335839,0.0001180352,0.00006328025,0.000004239124,0.00005598372,0.8570539,0.09884898,0.00257865,0.03868019,0.000540273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006647069,0.0001102801,0.9820291,0.007178893,0.0003219703,0.0006601129,0.00006298533,0.0001881247,0.00280151],"genre_scores_gemma":[0.9580132,0.0004917174,0.03992826,0.0005971659,0.0000351964,0.0006973277,0.00002737554,0.0000211519,0.0001886262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9513661,"threshold_uncertainty_score":0.9968672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08509650198207858,"score_gpt":0.3577742359128073,"score_spread":0.2726777339307287,"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."}}