{"id":"W4383960525","doi":"10.1109/tvt.2023.3293189","title":"NoncovANM: Gridless DOA Estimation for LPDF System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computational complexity theory; Algorithm; Direction of arrival; Channel (broadcasting); Cramér–Rao bound; Saddle point; Norm (philosophy); Estimation theory; Mathematics; Telecommunications; Antenna (radio)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004094467,0.0008077699,0.0005316855,0.0005312579,0.0003467792,0.0005606731,0.0009798734,0.0006719358,0.002095806],"category_scores_gemma":[0.002565135,0.0002691774,0.0004815109,0.001073455,0.000472559,0.0008383828,0.0008903214,0.0008835634,0.001006206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004134621,"about_ca_system_score_gemma":0.0008747022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003743414,"about_ca_topic_score_gemma":0.003955105,"domain_scores_codex":[0.9995185,0.0001138255,0.00002163821,0.0001216772,0.0001930477,0.0000312768],"domain_scores_gemma":[0.9995649,0.0001584122,0.00005780658,0.00009575298,0.0001055121,0.00001760578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002051943,0.00007210847,0.002205529,0.0002745221,0.00006702782,0.0001601845,0.0001337501,0.4090353,0.01639856,0.02014411,0.006395757,0.544908],"study_design_scores_gemma":[0.00001119305,0.00003082124,0.0004403562,0.00001169268,0.000006682038,0.0001048863,0.00001594058,0.9881044,0.002584742,0.004932285,0.003744022,0.0000130661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002871311,0.0001789545,0.9957926,0.0000594604,0.00004277103,0.0000145965,0.00005627327,0.0002414809,0.0007425236],"genre_scores_gemma":[0.2740264,0.001126796,0.7183341,0.0002142321,0.000176391,0.000276596,0.0008399981,0.000184041,0.004821411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003743414,"threshold_uncertainty_score":0.007443249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033020497487199,"score_gpt":0.2281450120959316,"score_spread":0.2178148071210596,"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."}}