{"id":"W1508948970","doi":"10.1109/vetecs.2006.1683360","title":"Experimental Antenna Array Calibration with ADAptive LInear Neuron (ADALINE) Network","year":2006,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial neural network; Calibration; Antenna (radio); Context (archaeology); Antenna array; Direction of arrival; Smart antenna; Electronic engineering; Algorithm; Directional antenna; Artificial intelligence; Engineering; Telecommunications; Mathematics","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":[0.0009781343,0.0004034804,0.0002620989,0.0003337942,0.0002390658,0.0003649002,0.0009105422,0.0007351717,0.002167726],"category_scores_gemma":[0.003489428,0.0002019455,0.0001503142,0.0006296401,0.0003952532,0.0007007549,0.00061719,0.0006450794,0.0003899353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003552233,"about_ca_system_score_gemma":0.0002198928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007402932,"about_ca_topic_score_gemma":0.0009082218,"domain_scores_codex":[0.9992869,0.0001907346,0.00004892339,0.0001684028,0.0002445377,0.00006042725],"domain_scores_gemma":[0.9984626,0.0005184093,0.0001358931,0.0002802993,0.000557164,0.00004569973],"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":[0.001787532,0.0007321874,0.01039742,0.0005130151,0.0001276307,0.0003201709,0.0007131985,0.2320836,0.533296,0.003964966,0.002363993,0.2137003],"study_design_scores_gemma":[0.0001071335,0.001262329,0.006480615,0.0000388124,0.00003544799,0.0002192645,0.0001434854,0.5620394,0.4255368,0.001199597,0.002885428,0.00005162946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6172391,0.0001532602,0.3739769,0.0002684605,0.0002127423,0.0001752788,0.0003003399,0.001447028,0.006226944],"genre_scores_gemma":[0.9495147,0.00005691111,0.0488558,0.0000500245,0.00001038514,0.00009236325,0.0001091666,0.00005086574,0.001259818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002167726,"threshold_uncertainty_score":0.007251799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01277302008639161,"score_gpt":0.2364586804223692,"score_spread":0.2236856603359776,"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."}}