{"id":"W4206091681","doi":"10.1109/lgrs.2021.3140097","title":"Broadband Soil Permittivity Measurements Using a Novel De-Embedding Line–Line Method","year":2022,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Notation; Line (geometry); Permittivity; Calibration; Dielectric; Embedding; Mathematics; Relative permittivity; Materials science; Mathematical analysis; Geometry; Computer science; Optoelectronics","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","sts"],"consensus_categories":[],"category_scores_codex":[0.001870451,0.0002998134,0.0002995456,0.000133916,0.001657592,0.0001270184,0.000241601,0.00007222676,0.00001660726],"category_scores_gemma":[0.00007123458,0.0002809489,0.0001147674,0.0006835568,0.0004476858,0.0002482081,0.0003410137,0.0004783173,0.000006505411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004737714,"about_ca_system_score_gemma":0.00005274153,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008405405,"about_ca_topic_score_gemma":0.0004593517,"domain_scores_codex":[0.9968708,0.0002658252,0.0003159071,0.0008326753,0.0009143251,0.000800475],"domain_scores_gemma":[0.9991226,0.0001101978,0.0001897647,0.0003457367,0.00001772777,0.0002140095],"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":[0.00001773411,0.00002494959,0.0005901306,0.000007404084,0.00001125243,0.00004730816,0.0008851747,0.08154725,0.7823864,1.896055e-7,0.0001187853,0.1343634],"study_design_scores_gemma":[0.0006079193,0.00007243672,0.006028108,0.0000603265,0.00007139063,0.001518597,0.000462415,0.9655235,0.02358716,0.00006834788,0.001446204,0.0005535305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6647704,0.00002930811,0.3327189,0.001063912,0.0006974072,0.0001287878,0.000001886374,0.00005392114,0.000535527],"genre_scores_gemma":[0.7715763,0.000007014266,0.2218293,0.006155218,0.0002052258,6.49979e-8,0.000001638239,0.00003171412,0.0001934945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8839763,"threshold_uncertainty_score":0.9999643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0416242483443021,"score_gpt":0.292500254916628,"score_spread":0.2508760065723258,"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."}}