{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001192366,0.0002096857,0.0002461589,0.001023572,0.0002187142,0.00002437912,0.0002623213,0.0005241732,0.000009280147],"category_scores_gemma":[0.00001184367,0.0002169167,0.0001174326,0.001415269,0.00009082119,0.00009109663,0.000001836981,0.0002825451,0.0003380329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001660023,"about_ca_system_score_gemma":0.00001413704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003501875,"about_ca_topic_score_gemma":0.00001117557,"domain_scores_codex":[0.9989328,0.00001038652,0.0002857013,0.000259598,0.0001400469,0.0003714894],"domain_scores_gemma":[0.9993775,0.00005737628,0.00003326459,0.0004209493,0.00007897455,0.00003192719],"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":[0.00001010203,0.00002230438,0.000004237916,0.0002402815,0.00007451597,0.00001481351,0.00004326504,0.9327388,0.00565498,0.004110203,0.0007480919,0.05633844],"study_design_scores_gemma":[0.0004295191,0.00007438027,0.000005451587,0.00005956029,0.00003860674,0.00002504725,0.0003577934,0.6686707,0.325896,0.0007852222,0.00344121,0.0002164864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03857589,0.0000460485,0.9464236,0.0004321572,0.001026507,0.00049987,0.0000539729,0.01279051,0.0001514416],"genre_scores_gemma":[0.9949936,0.0000588297,0.004033799,0.00001822641,0.0000228821,0.0006441924,0.00002075966,0.00006473083,0.0001429286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9564177,"threshold_uncertainty_score":0.8845606,"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."}}