{"id":"W3137802961","doi":"10.1002/jnm.2879","title":"Recent advances in<scp>knowledge‐based</scp>model structure optimization and extrapolation techniques for microwave applications","year":2021,"lang":"en","type":"article","venue":"International Journal of Numerical Modelling Electronic Networks Devices and Fields","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Extrapolation; Artificial neural network; Computer science; Microwave; Range (aeronautics); CAD; Artificial intelligence; Microwave imaging; Machine learning; Engineering; Engineering drawing; Mathematics","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":[0.00009986755,0.0001104373,0.0001512321,0.00009061168,0.00002905953,0.00005366992,0.00008935743,0.0001144634,0.000001653596],"category_scores_gemma":[0.00001298005,0.0001091165,0.00004072059,0.0001041855,0.00001208525,0.000158634,0.00001216073,0.0002746246,2.988024e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006466598,"about_ca_system_score_gemma":0.00004396949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.144309e-7,"about_ca_topic_score_gemma":0.00001156725,"domain_scores_codex":[0.999325,0.00001086943,0.0002955884,0.0001202747,0.00008121014,0.0001670221],"domain_scores_gemma":[0.9994428,0.0001550536,0.00007945597,0.00004874165,0.0002289765,0.00004495808],"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":[0.000004587856,0.00001239534,0.00003579774,0.00002573992,0.00002897276,7.514193e-7,0.00004924736,0.9482338,0.00005099745,0.0006517061,0.00004349452,0.0508625],"study_design_scores_gemma":[0.0001862993,0.00003373336,0.000004707242,0.00007178335,0.00001663831,0.00003007795,0.0000168259,0.9773149,0.0002807702,0.002899387,0.01908486,0.00006003966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005678936,0.05147238,0.9475484,0.00012164,0.0001144359,0.00007833182,0.000003955613,0.00002346143,0.00006951032],"genre_scores_gemma":[0.9030981,0.03211809,0.06442935,0.00006675686,0.0002339361,0.000009408242,0.00002049273,0.00001646252,0.000007414871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9025302,"threshold_uncertainty_score":0.4449642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008083131617179135,"score_gpt":0.2450736447928042,"score_spread":0.236990513175625,"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."}}