{"id":"W2132601884","doi":"10.1109/icc.2007.905","title":"Statistical Pruning for Near Maximum Likelihood Detection of MIMO Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; National Science Foundation","keywords":"Pruning; Computer science; Tree (set theory); MIMO; Algorithm; Probability distribution; Mathematics; Artificial intelligence; Statistics; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001966277,0.0004980056,0.0007679178,0.0009223498,0.0005004294,0.001012379,0.00070842,0.0007341428,0.0008708925],"category_scores_gemma":[0.008485023,0.0003484203,0.0004733295,0.00105301,0.0009554009,0.001045764,0.0009636993,0.0008275917,0.0003764067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006736924,"about_ca_system_score_gemma":0.000862535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004957375,"about_ca_topic_score_gemma":0.000900339,"domain_scores_codex":[0.9975678,0.0008138751,0.0000983624,0.0001201983,0.001277488,0.0001222943],"domain_scores_gemma":[0.9958816,0.003048977,0.0002710129,0.0003262945,0.000414968,0.0000572465],"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":[0.0001774457,0.00005645639,0.001121123,0.0002345738,0.00008480654,0.000395475,0.0001922932,0.5278711,0.02779492,0.2445645,0.002649943,0.1948573],"study_design_scores_gemma":[0.00001326158,0.0000419123,0.0003092012,0.00001872677,0.00001440502,0.0001998578,0.000007693607,0.9320297,0.006584053,0.05851461,0.002248408,0.00001819385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007469745,0.0004009072,0.9905324,0.0001188982,0.00001651007,0.00001178266,0.00001964264,0.0001286839,0.001301455],"genre_scores_gemma":[0.462537,0.001572199,0.5327727,0.000311013,0.0002129581,0.0001736821,0.0001641352,0.0001428844,0.002113487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001966277,"threshold_uncertainty_score":0.01039881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104532118126953,"score_gpt":0.2605445696369773,"score_spread":0.2494992484557078,"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."}}