{"id":"W2150470922","doi":"10.1109/ccece.2007.272","title":"Mobile Location in MIMO Communication Systems by Using Learning Machine","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Multilateration; Trilateration; MIMO; Multipath propagation; Computer science; Base station; Angle of arrival; Real-time computing; Wireless; Mobile station; Redundancy (engineering); Electronic engineering; Antenna (radio); Computer network; Telecommunications; Engineering; Beamforming","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":[],"consensus_categories":[],"category_scores_codex":[0.000275982,0.00007206228,0.00008308597,0.0001359728,0.0000485886,0.00002317501,0.0001046097,0.00009301671,0.00001268865],"category_scores_gemma":[0.00002314588,0.00007387802,0.000009993583,0.0003462773,0.00001844195,0.0001023243,0.0000194771,0.0001543291,0.00001208256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209838,"about_ca_system_score_gemma":0.000003682751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000345123,"about_ca_topic_score_gemma":0.00007621617,"domain_scores_codex":[0.9994953,0.00001722119,0.0002080594,0.00006741954,0.00006970765,0.0001422964],"domain_scores_gemma":[0.9997424,0.00003006197,0.0000232152,0.0001569101,0.00003230182,0.00001507246],"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":[0.000002444757,0.00001359219,0.006619472,0.00004654868,0.000005578376,7.045594e-7,0.000279693,0.9760034,0.006351202,0.00129551,0.0002885079,0.009093337],"study_design_scores_gemma":[0.0001739625,0.00001768926,0.0001542637,0.00003781554,0.000002642424,0.000003060407,0.001426529,0.976194,0.01634288,0.0000234468,0.005500215,0.0001234972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3088028,0.005279887,0.6776051,0.000005240566,0.0001111596,0.0002251273,0.000001024051,0.0009974993,0.006972179],"genre_scores_gemma":[0.9986601,0.0001585402,0.0009286122,0.000005889418,0.000006938136,0.000009165684,0.00003108958,0.00001656076,0.0001830665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6898574,"threshold_uncertainty_score":0.3012658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00834866286312608,"score_gpt":0.2324130324113963,"score_spread":0.2240643695482702,"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."}}