{"id":"W4220783810","doi":"10.18280/i2m.210102","title":"Artificial Neural Networks Oriented Testbed for Multiantenna Wireless Application","year":2022,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Testbed; Computer science; Artificial neural network; Feed forward; Feedforward neural network; Backpropagation; Wireless; Algorithm; Wireless network; Mean squared error; Computational complexity theory; Artificial intelligence; Mathematics; Engineering; Computer network; Telecommunications; Statistics; Control engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004612058,0.0004264186,0.0002553017,0.000262816,0.0002188837,0.0004287623,0.0007213362,0.0004213931,0.002269419],"category_scores_gemma":[0.0007476488,0.0001091327,0.0002150294,0.0002138009,0.0001988262,0.0004499157,0.000348589,0.0004895105,0.0004875087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003304084,"about_ca_system_score_gemma":0.000314378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007348043,"about_ca_topic_score_gemma":0.0008202459,"domain_scores_codex":[0.9996823,0.00008502352,0.00002056957,0.00004535937,0.0001298698,0.00003676166],"domain_scores_gemma":[0.9996293,0.0001002554,0.000046508,0.00007190891,0.000121098,0.00003099629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008413395,0.0007237761,0.007882658,0.0008692489,0.0001724109,0.0008947181,0.0001806974,0.4978008,0.2761417,0.02064823,0.00972579,0.1841186],"study_design_scores_gemma":[0.00004239335,0.001040229,0.003091807,0.00004677088,0.00003629299,0.0002381036,0.00007582625,0.8197618,0.1557569,0.004703268,0.01517279,0.00003378113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2785451,0.0005911064,0.6928179,0.0005648828,0.0005509354,0.0003799974,0.0009110648,0.004991573,0.02064757],"genre_scores_gemma":[0.8869091,0.0002447524,0.1056147,0.00009113994,0.00001993545,0.0003293843,0.0005678115,0.00007506783,0.006148028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002269419,"threshold_uncertainty_score":0.007591963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02481264831662851,"score_gpt":0.2840733340193703,"score_spread":0.2592606857027419,"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."}}