{"id":"W2525523245","doi":"10.1038/ncomms13004","title":"Wideband dynamic microwave frequency identification system using a low-power ultracompact silicon photonic chip","year":2016,"lang":"en","type":"article","venue":"Nature Communications","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; CMC Microsystems","keywords":"Photonics; Broadband; Wideband; Computer science; Fiber Bragg grating; Electronic engineering; High dynamic range; Chip; Microwave; Silicon photonics; Dynamic range; Optoelectronics; Materials science; Telecommunications; Engineering; Optical fiber","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":[0.0002059874,0.0002046965,0.0002178872,0.0001220245,0.0002062433,0.00006848823,0.0009377783,0.0003187177,0.00002817998],"category_scores_gemma":[0.00007816641,0.0001599389,0.0001063946,0.0002981591,0.0001274719,0.0003053406,0.00007339399,0.000570061,0.0001224459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004524969,"about_ca_system_score_gemma":0.00004850712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001845711,"about_ca_topic_score_gemma":0.000235824,"domain_scores_codex":[0.9988726,0.00007797821,0.0003890793,0.0002152165,0.0001572352,0.0002878723],"domain_scores_gemma":[0.9976259,0.0002936564,0.0000921256,0.001772272,0.0001113072,0.0001046739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003326375,0.0000433097,0.0002931189,0.00006709569,0.00005678809,0.000001474724,0.0001435189,0.00007030583,0.9877035,0.01095248,0.00006386177,0.0006012357],"study_design_scores_gemma":[0.003102777,0.00008198553,0.06926423,0.003911783,0.000588008,0.0003866218,0.001102293,0.4759405,0.428998,0.003287647,0.01028609,0.003050126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435368,0.01916956,0.008918637,0.0005769315,0.0007774373,0.000648606,0.0001485479,0.000892482,0.02533101],"genre_scores_gemma":[0.9968517,0.0005425769,0.002379358,0.00004714423,0.00001702004,0.00003553877,0.00003718562,0.00004407019,0.00004539958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5587055,"threshold_uncertainty_score":0.6522121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045508277178711,"score_gpt":0.2576433851589208,"score_spread":0.2471883023871337,"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."}}