{"id":"W2803369220","doi":"10.1175/jtech-d-17-0175.1","title":"A Simple and Effective Method for Separating Meteorological from Nonmeteorological Targets Using Dual-Polarization Data","year":2018,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Environment and Climate Change Canada; Government of Canada; National Science Foundation","keywords":"Remote sensing; Flagging; Computer science; Radar; Dual-polarization interferometry; Weather radar; Environmental science; Echo (communications protocol); Polarization (electrochemistry); Meteorology; Geology; Physics; Telecommunications; Cartography","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":[0.0009856775,0.0001133965,0.0003278132,0.00002325872,0.0001970919,0.00003697077,0.0001812887,0.0001702635,0.0001598834],"category_scores_gemma":[0.0006909875,0.00007899149,0.00003845527,0.0002691057,0.0001624121,0.0002526566,0.00005128285,0.000155603,0.00000131286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000507556,"about_ca_system_score_gemma":0.00002544831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000487209,"about_ca_topic_score_gemma":0.00005065735,"domain_scores_codex":[0.9989641,0.000129172,0.0003487848,0.0002481989,0.0001316871,0.0001780577],"domain_scores_gemma":[0.9988921,0.000423614,0.0003615202,0.0001274926,0.0001291733,0.00006604238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003774945,0.00003938314,0.6455313,0.00001321997,0.0004366718,0.00001799937,0.0001831532,0.0002528875,0.01678562,0.0002025549,0.000225505,0.3359342],"study_design_scores_gemma":[0.0009798898,0.001758344,0.265883,0.00001457915,0.0003884012,0.0001043287,0.0004454479,0.7095533,0.000639932,0.01868234,0.001365352,0.0001850636],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6909135,0.001456598,0.3072462,0.0001631073,0.00006746491,0.00009629539,0.00002521193,0.00001488684,0.00001673284],"genre_scores_gemma":[0.6818103,0.00004433536,0.3179051,0.0001001859,0.000119976,2.275887e-7,0.00001532612,0.000002063506,0.000002456182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7093004,"threshold_uncertainty_score":0.322118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913932223840478,"score_gpt":0.2935884569848918,"score_spread":0.264449134746487,"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."}}