{"id":"W4390604729","doi":"10.1109/tcyb.2023.3341804","title":"Dynamic Hybrid Models With Active Sampling and Adaptive Selection of Double-Domain Features for the Tuning of Microwave Cavity Filters","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Higher Education Discipline Innovation Project; China Scholarship Council; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Microwave; Sampling (signal processing); Adaptive sampling; Electronic engineering; Computer science; Domain (mathematical analysis); Biological system; Materials science; Selection (genetic algorithm); Microwave cavity; Control theory (sociology); Filter (signal processing); Engineering; Mathematics; Artificial intelligence; Telecommunications; Biology; Mathematical analysis; Statistics","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":[0.000258142,0.000420076,0.0004383604,0.000279171,0.0001933446,0.0004483455,0.0006829131,0.0004843834,0.001107445],"category_scores_gemma":[0.0005786427,0.0002446687,0.0004961133,0.0002276238,0.0003316031,0.0005092426,0.0004651046,0.0004729279,0.0002473977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002836753,"about_ca_system_score_gemma":0.0002886191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002481808,"about_ca_topic_score_gemma":0.002675543,"domain_scores_codex":[0.9998845,0.00002503664,0.000006225815,0.00003049007,0.00003903095,0.00001477704],"domain_scores_gemma":[0.999856,0.00006252702,0.00002328878,0.00001675957,0.00003482151,0.00000670339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004653105,0.0000249608,0.000567927,0.00003654567,0.00001999185,0.00003624502,0.00005280474,0.9607353,0.00744918,0.003855168,0.0002864488,0.02688887],"study_design_scores_gemma":[0.000001096729,0.000005184146,0.00003589109,0.00000114068,0.00000176184,0.000002742584,0.000001462321,0.9992509,0.0003160955,0.000247885,0.0001344951,0.000001254897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03712519,0.0002502889,0.9588223,0.00007399063,0.0000273662,0.0000258224,0.00003844705,0.0002876737,0.003348961],"genre_scores_gemma":[0.9418415,0.0001728646,0.05416321,0.00004657555,0.00001786354,0.0001308658,0.0000833606,0.00003694982,0.003506678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002481808,"threshold_uncertainty_score":0.004934728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145338524082256,"score_gpt":0.2505456634201279,"score_spread":0.2290922781793053,"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."}}