{"id":"W1916447172","doi":"10.48550/arxiv.1001.3403","title":"Real Interference Alignment","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interference alignment; Dirty paper coding; Coding (social sciences); Interference (communication); Computer science; Topology (electrical circuits); Gaussian; Multiplexing; Degenerate energy levels; Data stream mining; Algorithm; Mathematics; Channel (broadcasting); Telecommunications; Precoding; Physics; Combinatorics; MIMO","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001229243,0.0002722073,0.0002582732,0.0001722552,0.00005216434,0.00004734035,0.001315315,0.000467148,0.0001176509],"category_scores_gemma":[0.000008971683,0.0003565853,0.0001197166,0.000149946,0.0001013322,0.0001085189,0.001154423,0.001259288,0.0000885528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00023998,"about_ca_system_score_gemma":0.00004062575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000192393,"about_ca_topic_score_gemma":0.0001830136,"domain_scores_codex":[0.9990969,0.00005179872,0.0001799806,0.0003939066,0.00005263177,0.0002248082],"domain_scores_gemma":[0.998153,0.00004961757,0.0000784008,0.00153519,0.00007294373,0.0001107872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000723753,0.0003587389,0.00452538,0.0009474781,0.0006753784,0.0003166803,0.002631917,0.3848116,0.01637997,0.5738673,0.009659808,0.005753408],"study_design_scores_gemma":[0.0005137003,0.00005957404,0.001731217,0.0004885055,0.0001607407,0.000007146816,0.0002370189,0.8864789,0.02332809,0.07405623,0.01107646,0.001862432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8689178,0.00005712041,0.08750233,0.00003983819,0.0005441596,0.0003240327,0.00004065261,0.002262325,0.04031177],"genre_scores_gemma":[0.9960568,0.002209347,0.001268874,0.00001334044,0.00004614811,0.000003155784,0.00004258003,0.00004294346,0.0003168228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5016673,"threshold_uncertainty_score":0.9998886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05730892935754662,"score_gpt":0.1941262029419885,"score_spread":0.1368172735844418,"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."}}