{"id":"W7117449753","doi":"10.1109/conecct65861.2025.11306477","title":"Near-field Characterization of Large-Scale mm-wave Massive MIMO Arrays using IDM Computed EM Lagrangian Density","year":2025,"lang":"","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Poynting vector; Isotropy; Lagrangian; Dimension (graph theory); Augmented Lagrangian method; Lagrangian and Eulerian specification of the flow field; Distribution (mathematics); Probability density function","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":[0.0001841629,0.0003657565,0.0007060361,0.0001895868,0.0004099429,0.0002125907,0.0002093565,0.0001687161,0.0008401877],"category_scores_gemma":[0.000007980625,0.0003863055,0.0004205208,0.0009776113,0.00006986259,0.0001330786,0.0001645555,0.0002942507,0.00001906897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004619402,"about_ca_system_score_gemma":0.0002043269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004180184,"about_ca_topic_score_gemma":0.00005643743,"domain_scores_codex":[0.9978093,0.0001438068,0.0006846922,0.0005651134,0.0002274596,0.0005696533],"domain_scores_gemma":[0.9985836,0.00009197503,0.0003823562,0.0005066721,0.0003121475,0.0001232739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001054242,0.0009181924,0.04301706,0.0002430148,0.001318271,0.000006614755,0.002226712,0.0007162426,0.9251354,0.002284324,0.0002715228,0.02375723],"study_design_scores_gemma":[0.00137452,0.0002573353,0.03565187,0.0005538696,0.001189512,0.000001156663,0.001641123,0.5708255,0.3869689,0.0004687366,0.0004170557,0.000650418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6311159,0.00002365797,0.3670181,0.0005884289,0.0001894292,0.0001577674,0.00003876932,0.00002140517,0.0008465093],"genre_scores_gemma":[0.9930931,0.000007952031,0.004295132,0.0003700466,0.0002059231,0.000004045747,0.0001952492,0.00002178991,0.00180675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5701092,"threshold_uncertainty_score":0.9998589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007756981449383258,"score_gpt":0.2296154852216711,"score_spread":0.2218585037722878,"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."}}