{"id":"W4411910264","doi":"10.54254/2977-3903/2025.24777","title":"Application of neural network algorithms in the design of electromagnetic parameters for dielectric loss microwave absorbing materials","year":2025,"lang":"en","type":"article","venue":"Advances in Engineering Innovation","topic":"Electromagnetic wave absorption materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Microwave; Artificial neural network; Dielectric; Dielectric loss; Materials science; Electronic engineering; Computer science; Algorithm; Acoustics; Engineering; Optoelectronics; Physics; Telecommunications; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00140693,0.0001425078,0.0002899421,0.0003135909,0.00002240624,0.00002246493,0.0002716354,0.0000682266,0.000003920304],"category_scores_gemma":[0.0002546326,0.0001297048,0.00001892989,0.001905872,0.00004281883,0.0001921374,0.00001929876,0.00006811511,5.607769e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006218183,"about_ca_system_score_gemma":0.00002980817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000172836,"about_ca_topic_score_gemma":0.000003461468,"domain_scores_codex":[0.9984164,0.00009451724,0.0008202508,0.0002259699,0.0001355993,0.0003072624],"domain_scores_gemma":[0.9988906,0.0004733109,0.0002929497,0.0002175852,0.0001198384,0.000005754623],"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":[0.00005993113,0.00002297816,0.00006787901,0.0001704014,0.000002201851,2.556628e-7,0.00004062479,0.1988936,0.7871842,0.01169688,0.000004876548,0.001856138],"study_design_scores_gemma":[0.0004549313,0.0002468732,0.002275871,0.0001013762,0.00001048508,0.000002770779,0.00001053818,0.03712927,0.9493401,0.01027546,0.00003094333,0.0001214087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6382006,0.0004584913,0.3603441,0.00004455321,0.0002081657,0.0007097436,0.000004164481,0.00001782116,0.00001228044],"genre_scores_gemma":[0.9392389,0.00007054552,0.06022535,0.00004231185,0.00003589298,0.0003502817,0.00001985197,0.00001278566,0.000004056422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3010383,"threshold_uncertainty_score":0.5289208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009318660833349281,"score_gpt":0.2524072206781209,"score_spread":0.2430885598447717,"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."}}