{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002141736,0.00006965206,0.000113719,0.0000532106,0.00003776482,0.00001123983,0.0001007688,0.00005848317,0.00000489746],"category_scores_gemma":[0.00003822919,0.00007253917,0.00002074955,0.00008740494,0.00002588972,0.00007152309,0.00001688933,0.00007672965,0.0000028246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004732196,"about_ca_system_score_gemma":0.000005188439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002321616,"about_ca_topic_score_gemma":0.00002347861,"domain_scores_codex":[0.9994542,0.000008060078,0.0002468581,0.00006682977,0.00007093367,0.0001531123],"domain_scores_gemma":[0.999451,0.0001892876,0.00003291541,0.0002254767,0.00006412216,0.00003715855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005805429,0.00006393548,0.0002539112,0.0008015989,0.00006889484,0.000001461473,0.0002996774,0.01581095,0.4152839,0.03499978,0.0004384283,0.5319194],"study_design_scores_gemma":[0.0003089143,0.0001224608,0.0006010425,0.00006535021,0.00001295704,0.000005789162,0.0002749919,0.3517998,0.6299851,0.004281676,0.01231073,0.0002311971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00933922,0.0001986369,0.9874932,0.000003547923,0.00008784479,0.0002687217,0.000007980794,0.0005986521,0.002002251],"genre_scores_gemma":[0.7915604,0.00001883991,0.2083318,0.000003484727,0.0000147853,0.00003015713,0.000005419143,0.00002158611,0.00001349577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7822212,"threshold_uncertainty_score":0.2958062,"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."}}