{"id":"W4403680335","doi":"10.3390/s24216810","title":"Multi-Objective Design and Optimization of Hardware-Friendly Grid-Based Sparse MIMO Arrays","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"U.S. Department of Veterans Affairs","keywords":"Beamwidth; Computer science; Beamforming; Sparse array; MIMO; Computer engineering; Grid; Key (lock); Electronic engineering; Antenna (radio); Algorithm; Engineering; Mathematics; Telecommunications","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":[0.0001041608,0.000129641,0.0001332807,0.0001445311,0.00002983191,0.00003262307,0.00003965884,0.00007589142,0.00003187078],"category_scores_gemma":[0.00003585888,0.0001267098,0.00003724627,0.0002320377,0.00004357027,0.0000961168,0.000005633639,0.00008379926,0.00001078115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003288394,"about_ca_system_score_gemma":0.00002150607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008376394,"about_ca_topic_score_gemma":0.000001638104,"domain_scores_codex":[0.9994097,0.00004006067,0.0001576424,0.0001650509,0.00009033527,0.0001371882],"domain_scores_gemma":[0.9996982,0.00008131717,0.00001866831,0.0001015295,0.0000524579,0.00004788678],"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":[0.00001554539,0.00001006402,0.00001893452,0.00008434084,0.00002709679,0.000009750239,0.0003970471,0.994803,0.003974281,0.00003323067,0.000293343,0.0003334361],"study_design_scores_gemma":[0.0002393692,0.00004296247,0.0000680028,0.00008091293,0.00002923833,0.000003590439,0.0001133181,0.9870368,0.01211261,0.000007569831,0.0001306862,0.0001348866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006357985,0.0004291226,0.9921379,0.00002256463,0.0002978148,0.000221413,0.0000162522,0.0003245571,0.0001923725],"genre_scores_gemma":[0.55723,0.0001683465,0.4422732,0.00001592887,0.00005788229,0.00001074664,0.00002655366,0.00005791791,0.0001594615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.550872,"threshold_uncertainty_score":0.5167074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634801538831709,"score_gpt":0.2190741758500977,"score_spread":0.2027261604617806,"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."}}