{"id":"W4210458800","doi":"10.1109/tap.2022.3145452","title":"An Adaptive Data Acquisition Technique to Enhance the Speed of Near-Field Antenna Measurement","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Interpolation (computer graphics); Field (mathematics); Algorithm; Notation; Computer science; Antenna (radio); Sampling (signal processing); Component (thermodynamics); Mathematics; Artificial intelligence; Pure mathematics; Physics; Computer vision; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000685106,0.0000984683,0.0001025222,0.00006060276,0.0002617016,0.00002262412,0.0001980647,0.00002480124,0.0000694641],"category_scores_gemma":[0.000005040087,0.00008613437,0.00002186615,0.0002337893,0.00002490516,0.0001568179,0.000003309254,0.000198762,0.000001544781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006482299,"about_ca_system_score_gemma":0.00003106485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008577891,"about_ca_topic_score_gemma":0.0001501305,"domain_scores_codex":[0.9990262,0.00009984773,0.0001926717,0.0002080488,0.0003481676,0.0001250341],"domain_scores_gemma":[0.9993849,0.00002385929,0.00003236554,0.0004196076,0.00009448128,0.00004478134],"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":[0.0002302203,0.0001510517,0.000005372719,0.00003023284,0.00002865224,6.372076e-7,0.0005054629,0.009325949,0.9392155,0.00001742968,0.00006030275,0.05042912],"study_design_scores_gemma":[0.0002336823,0.002921938,0.0005869652,0.00007709434,0.00005521975,0.00001265297,0.00039892,0.3235511,0.6715543,0.0001668201,0.0002326081,0.0002086608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1294865,0.00008709108,0.868993,0.0003110285,0.0001603235,0.0007597386,0.00004742985,0.00007450648,0.00008029061],"genre_scores_gemma":[0.998917,0.00002824612,0.0008019604,0.0001156224,0.00001299686,0.00009230833,0.000007235036,0.00001184115,0.00001279291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8694305,"threshold_uncertainty_score":0.3512458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421994786265194,"score_gpt":0.2625987767786334,"score_spread":0.2283788289159814,"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."}}