{"id":"W2050507001","doi":"10.3390/s140609669","title":"Precise Calibration of a GNSS Antenna Array for Adaptive Beamforming Applications","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Calibration; Beamforming; Computer science; Antenna (radio); Antenna array; Electronic engineering; Interference (communication); Global Positioning System; GPS signals; Noise (video); Engineering; Telecommunications; Assisted GPS; Channel (broadcasting); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449574,0.00100153,0.0005796797,0.0006313229,0.0003402944,0.0005977816,0.0006404857,0.0008497317,0.001332604],"category_scores_gemma":[0.002387059,0.0003652871,0.0003929568,0.0008195373,0.0003838055,0.0009412698,0.0006996178,0.0009998484,0.001434028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004189744,"about_ca_system_score_gemma":0.0006565183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144175,"about_ca_topic_score_gemma":0.001972337,"domain_scores_codex":[0.9990115,0.0002539149,0.00004826482,0.0001699333,0.0004724399,0.00004392169],"domain_scores_gemma":[0.9991643,0.0001905848,0.0001322022,0.0001996645,0.0002936522,0.00001955065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001738459,0.00005605646,0.003602078,0.0002371823,0.00008617612,0.00009187259,0.0002408572,0.1410415,0.3689482,0.007961025,0.002237861,0.4753233],"study_design_scores_gemma":[0.0000598583,0.000337531,0.008766434,0.00009911522,0.00007583956,0.0005620221,0.0001575114,0.6762412,0.2826513,0.007061202,0.02384593,0.0001420643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007211597,0.0001211372,0.9911424,0.00006512697,0.0000313799,0.00002222987,0.00003545771,0.0004001749,0.0009704251],"genre_scores_gemma":[0.2870285,0.0004455755,0.7098151,0.000180955,0.00007100451,0.0001440318,0.000330632,0.0001813496,0.001802765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001332604,"threshold_uncertainty_score":0.00445801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696874679609427,"score_gpt":0.261891165710614,"score_spread":0.2449224189145197,"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."}}