{"id":"W2550139263","doi":"10.33012/2016.13487","title":"Performance of Antenna Array Calibration in Multipath Environments","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; GNSS applications; Multipath mitigation; Calibration; Computer science; Antenna (radio); Beamforming; Multipath interference; Interference (communication); Remote sensing; Antenna array; Electronic engineering; Global Positioning System; Telecommunications; Engineering; Geography; Physics","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":[0.00130957,0.000403,0.0006679804,0.0003406567,0.000108679,0.0000260552,0.002811181,0.000379581,0.000008106937],"category_scores_gemma":[0.002432766,0.000268147,0.0004862479,0.001053726,0.001306316,0.001435467,0.0006566024,0.0004990734,9.259579e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004225533,"about_ca_system_score_gemma":0.0001052034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007547905,"about_ca_topic_score_gemma":0.000007559987,"domain_scores_codex":[0.9948138,0.00001518766,0.002554233,0.0004587624,0.001878979,0.0002790665],"domain_scores_gemma":[0.9948747,0.0001582452,0.003103298,0.000316934,0.001481815,0.00006507355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002165639,0.0002556079,0.03031076,0.0004488385,0.00009834061,1.604463e-7,0.0001040581,0.006545981,0.9512946,0.01000317,0.0001135217,0.0006083863],"study_design_scores_gemma":[0.00117991,0.0001052332,0.01833213,0.01080592,0.00009688454,0.00003113338,0.00007653363,0.01404937,0.9515474,0.003168835,0.0003246791,0.0002819334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902516,0.00004872267,0.0008470928,0.001446739,0.001518124,0.0008016452,0.0001099736,0.00007824949,0.004897828],"genre_scores_gemma":[0.9870478,0.0001903965,0.01239604,0.00002462112,0.0001153468,0.00004742762,0.00001746195,0.00004816599,0.0001127353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01197863,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101438604718928,"score_gpt":0.2215232895080723,"score_spread":0.210508903460883,"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."}}