{"id":"W3030263417","doi":"10.3390/s20113079","title":"Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Algorithm; Range (aeronautics); Noise (video); Estimation theory; Interferometry; Computer science; Bayesian probability; Microwave; Mathematics; Statistics; Artificial intelligence; Engineering; Physics; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006745217,0.0002697572,0.0002976644,0.0001122141,0.00006654179,0.00005729083,0.00009380207,0.0001560253,0.00001838847],"category_scores_gemma":[0.0001583171,0.0003004334,0.0001265747,0.000300439,0.00005219898,0.00008881862,0.0000164741,0.0002404108,0.0000291875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111676,"about_ca_system_score_gemma":0.00001691413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001261276,"about_ca_topic_score_gemma":0.000001914681,"domain_scores_codex":[0.998724,0.0000209029,0.0003386257,0.0003489517,0.0001613426,0.0004061298],"domain_scores_gemma":[0.9991965,0.0002152124,0.00003750277,0.0002256562,0.00008511062,0.000239972],"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":[0.000140544,0.00003190276,0.00002669738,0.000337881,0.0002285004,0.0001593652,0.002596448,0.3108965,0.3689425,0.0002607432,0.001805895,0.3145731],"study_design_scores_gemma":[0.000529898,0.00008037979,0.00001409947,0.00002838213,0.00002717732,0.00001138174,0.0001523766,0.7754717,0.2213719,0.00005704644,0.001980291,0.0002753623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1903665,0.00004543414,0.8072369,0.0003923025,0.0004446328,0.0003519598,0.00005203562,0.0005367986,0.0005735232],"genre_scores_gemma":[0.5886675,0.000002220788,0.4105038,0.0003240829,0.0003101223,0.000004339648,0.00004912683,0.00009631358,0.00004257785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4645752,"threshold_uncertainty_score":0.9999448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407588185468258,"score_gpt":0.231183703300023,"score_spread":0.2171078214453404,"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."}}