{"id":"W2511693717","doi":"10.1371/journal.pone.0160849","title":"Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Radar; Microwave imaging; Permittivity; Computer science; Microwave; Polynomial; Algorithm; Collocation (remote sensing); Fitness function; Mathematical optimization; Mathematics; Physics; Machine learning; Mathematical analysis; Telecommunications; Dielectric; Genetic algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003857359,0.0001669014,0.0002514494,0.00005693901,0.0001871296,0.00007304902,0.0001676245,0.00003904496,0.00001916609],"category_scores_gemma":[0.0003529433,0.0001056744,0.0001055439,0.0001069293,0.00007611471,0.000141298,0.00003429416,0.0001230296,0.00003893394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006569832,"about_ca_system_score_gemma":0.000007829096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000092226,"about_ca_topic_score_gemma":0.000003924612,"domain_scores_codex":[0.999121,0.00005173717,0.0002004008,0.0002205691,0.0001039235,0.000302448],"domain_scores_gemma":[0.9988655,0.0006819001,0.0000593313,0.0002855167,0.00006314677,0.00004460154],"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":[0.000004409196,0.00002792656,0.001492094,0.0001498594,0.0003551788,0.000001046009,0.0001505188,0.0001102836,0.9277477,0.000005380078,0.0002472965,0.06970826],"study_design_scores_gemma":[0.0003851272,0.00002297694,0.0009148361,0.0005572682,0.000266164,0.00001171112,0.00004183708,0.05696112,0.9400856,0.0004011756,0.00005750828,0.0002946423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8877751,0.0001543442,0.1101695,0.0006499031,0.0001055545,0.0002796053,0.00003965891,0.0003053468,0.0005209808],"genre_scores_gemma":[0.9487408,0.00001062178,0.05067148,0.00003058681,0.0002290472,0.00008613299,0.000002516314,0.00004106875,0.0001877015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06941361,"threshold_uncertainty_score":0.4309279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02125662443237288,"score_gpt":0.2205325652538416,"score_spread":0.1992759408214687,"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."}}