{"id":"W2014804400","doi":"10.2166/hydro.2010.056","title":"Towards better utilization of NEXRAD data in hydrology: an overview of Hydro-NEXRAD","year":2010,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University Corporation for Atmospheric Research; National Science Foundation","keywords":"Hydrometeorology; Radar; Environmental science; Meteorology; Remote sensing; Computer science; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00178886,0.0001001122,0.0003652739,0.0003533979,0.00002723799,0.00002202074,0.0006314472,0.00007684524,0.0005521347],"category_scores_gemma":[0.0002211904,0.00007712734,0.00007920346,0.0003515125,0.00007098701,0.001641828,0.00002375927,0.0002550316,0.000008725165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003063037,"about_ca_system_score_gemma":0.0001130372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000114913,"about_ca_topic_score_gemma":0.001366349,"domain_scores_codex":[0.9980296,0.00007023074,0.001131032,0.0000672368,0.0005577528,0.0001441199],"domain_scores_gemma":[0.9983561,0.00007161236,0.001009099,0.0003644609,0.0001114306,0.00008731821],"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":[0.0001414095,0.0002532082,0.8514532,0.0005022679,0.0002293502,0.00001360376,0.005557356,0.02403647,0.002218573,0.0002160005,0.0008053719,0.1145732],"study_design_scores_gemma":[0.0006719814,0.0002876446,0.2059112,0.00008893527,0.0001178437,0.00003959601,0.0003738081,0.7882794,0.0008336494,0.002001363,0.001260339,0.0001342753],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997353,0.0003920246,0.000182586,0.00021638,0.0002031346,0.00005662487,0.00004913396,0.000003298583,0.001543821],"genre_scores_gemma":[0.9936705,0.0003467558,0.005648989,0.000140928,0.00006340514,5.616344e-8,0.0001218316,0.000002290949,0.000005247088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7642429,"threshold_uncertainty_score":0.6045488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1220614014423749,"score_gpt":0.3198690472201267,"score_spread":0.1978076457777517,"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."}}