{"id":"W2298956148","doi":"","title":"The US MOPEX data set.","year":2006,"lang":"en","type":"article","venue":"University of North Texas Digital Library (University of North Texas)","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"Hydrometeorology; Environmental science; Meteorology; Hydrological modelling; Climatology; Drainage basin; Structural basin; Estimation; Surface runoff; Climate model; Range (aeronautics); Water resources; Hydrology (agriculture); Precipitation; Climate change; Geography; Geology; Engineering; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001137225,0.0001903665,0.0002882488,0.0002012743,0.0005279012,0.00009278905,0.00205955,0.00005617458,0.001220574],"category_scores_gemma":[0.00001610709,0.0001945781,0.0001870046,0.0007604028,0.0005234556,0.003897754,0.0003179974,0.0001540897,0.0003491216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007163919,"about_ca_system_score_gemma":0.0001376829,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002565424,"about_ca_topic_score_gemma":0.03422788,"domain_scores_codex":[0.9984943,0.00007953837,0.0001914997,0.0004135821,0.0005094169,0.000311664],"domain_scores_gemma":[0.9985935,0.0001866018,0.000317841,0.0006622485,0.00008941864,0.0001503764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001110698,0.00004480524,0.9678084,0.00001787163,0.0001021609,0.00003765064,0.00001371595,0.001273877,3.65758e-8,0.00003894953,0.01284469,0.0177068],"study_design_scores_gemma":[0.0003785607,0.00006130078,0.885805,0.00001307617,0.00008541722,0.000001563578,0.00005836193,0.001951701,0.000001612464,0.00008988175,0.1113555,0.0001980558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718723,0.0001812651,0.000246124,0.0004069729,0.00006335261,0.0001476978,0.003684911,0.00008122907,0.02331619],"genre_scores_gemma":[0.9838817,0.0001932473,0.0004509552,0.00002762111,0.00003917163,1.465766e-9,0.004948816,0.000005160382,0.01045332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09851079,"threshold_uncertainty_score":0.9996924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873000071923767,"score_gpt":0.1659938626434569,"score_spread":0.1472638619242192,"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."}}