{"id":"W2344093235","doi":"10.1109/lawp.2016.2528221","title":"A Novel Wideband Frequency Selective Surface for Millimeter-Wave Applications","year":2016,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Wideband; Extremely high frequency; Stopband; Selective surface; Electromagnetic shielding; Coupling (piping); Attenuation; Materials science; Tunable metamaterials; Acoustics; Radio spectrum; Reduction (mathematics); Antenna (radio); Optics; Electronic engineering; Physics; Optoelectronics; Electrical engineering; Computer science; Band-pass filter; Engineering; Telecommunications; Metamaterial","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":[0.00008358053,0.000194205,0.0002042906,0.00007365843,0.0001206141,0.00002625508,0.00008825237,0.00008140014,0.000001986935],"category_scores_gemma":[0.00001666767,0.0001400537,0.00005445894,0.0001693052,0.0001398159,0.0002703985,0.00001020926,0.00008128406,0.000005485389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005095579,"about_ca_system_score_gemma":0.000009720898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005700701,"about_ca_topic_score_gemma":0.000007863796,"domain_scores_codex":[0.9991129,0.000007763188,0.0002151265,0.0002793481,0.00009110322,0.0002937682],"domain_scores_gemma":[0.9995105,0.0001043633,0.000060981,0.0001787336,0.00009381486,0.00005163412],"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":[0.000009157105,0.00001304425,0.0001475653,0.00004766088,0.00003991805,9.335488e-7,0.0000560144,0.0001723188,0.9840645,0.0004814735,0.0002201855,0.01474728],"study_design_scores_gemma":[0.001908951,0.0001114278,0.0007327332,0.0001841796,0.00005971179,0.00003493731,0.0001415122,0.009387926,0.9821824,0.002361522,0.002213832,0.000680841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3920939,0.0001584416,0.6058603,0.0009630831,0.00009306682,0.0004497874,0.00004926223,0.0003116212,0.00002057013],"genre_scores_gemma":[0.9867786,0.0003443511,0.01228336,0.0001933235,0.00005406088,0.0002405757,0.000006615348,0.00003947685,0.0000596351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5946847,"threshold_uncertainty_score":0.5711223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848314641049707,"score_gpt":0.2228792896147639,"score_spread":0.2043961432042669,"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."}}