{"id":"W2123621494","doi":"10.14796/jwmm.r223-17","title":"Rainfall Accuracy Considerations Using Radar and Rain Gauge Networks for Rainfall-Runoff Monitoring","year":2005,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Oklahoma Center for the Advancement of Science and Technology","keywords":"Rain gauge; Radar; Environmental science; Meteorology; Surface runoff; Remote sensing; Gauge (firearms); Runoff model; Hydrology (agriculture); Computer science; Geography; Geology; Telecommunications; Geotechnical engineering; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274884,0.0001354679,0.0002156496,0.0002574855,0.0003233178,0.0002421241,0.0001122645,0.00003771498,0.0001309729],"category_scores_gemma":[0.00004051728,0.00009999247,0.0001209402,0.00007329102,0.00001711024,0.0007810578,0.00001762072,0.0001152699,0.000002927286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001524823,"about_ca_system_score_gemma":0.00001366661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003141124,"about_ca_topic_score_gemma":0.00004189331,"domain_scores_codex":[0.9986647,0.00005867624,0.0005581767,0.0001497097,0.0002926613,0.0002761137],"domain_scores_gemma":[0.9993775,0.00009988605,0.0001787044,0.00009044709,0.0001511451,0.0001022655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003338266,0.000009501521,0.003131695,0.00002050608,0.0001728534,0.000006018268,0.000434558,0.9841686,0.0002327635,0.00004187196,0.0001254328,0.01162278],"study_design_scores_gemma":[0.0007823408,0.00003048399,0.0004014066,0.00006849263,0.000209814,0.00001135199,0.0003732338,0.9950854,0.0001244522,0.002039767,0.0007282232,0.0001450205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5649564,0.001025442,0.4317867,0.001189676,0.0003377752,0.0002389798,0.000004753641,0.00001620512,0.0004440808],"genre_scores_gemma":[0.9299402,0.0002347362,0.06888388,0.0002009844,0.0006464807,7.392778e-7,0.00001225758,0.0000056078,0.0000751753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3649837,"threshold_uncertainty_score":0.4077575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05678404705740115,"score_gpt":0.2651528324428378,"score_spread":0.2083687853854366,"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."}}