{"id":"W2132019270","doi":"10.5589/m04-037","title":"Investigation of the nonlinear hydrologic response to precipitation forcing in physically based land surface modeling","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Goddard Space Flight Center; Office of Science; National Oceanic and Atmospheric Administration; National Center for Atmospheric Research; National Aeronautics and Space Administration","keywords":"Environmental science; Precipitation; Forcing (mathematics); Rain gauge; Land cover; Climatology; Satellite; Meteorology; Atmospheric sciences; Land use; Geography; Geology","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":[0.001085849,0.00006176292,0.0001272067,0.0002181471,0.00008778802,0.00002903039,0.00009819029,0.00003295319,0.000008664966],"category_scores_gemma":[0.0004775547,0.00004727096,0.00006148026,0.0004518539,0.00002821941,0.0001271855,0.000001409902,0.0001201646,0.000003418136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000415086,"about_ca_system_score_gemma":0.000747306,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01540337,"about_ca_topic_score_gemma":0.1887431,"domain_scores_codex":[0.9991156,0.0001798535,0.0002815291,0.00007878024,0.0002025625,0.0001416159],"domain_scores_gemma":[0.9993343,0.00008916351,0.0001588043,0.00007991665,0.0001675055,0.0001703055],"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":[0.00005297605,4.30378e-7,0.03185691,0.000003567448,0.000005128494,0.000006375604,0.0009432173,0.9560046,0.003197793,2.748686e-7,0.000001759577,0.007927036],"study_design_scores_gemma":[0.0003222104,0.00005289847,0.05868723,0.0002256536,0.00001764225,0.000005351556,0.0001775067,0.9375851,0.002019059,0.000829832,0.00001422761,0.00006330663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977505,0.00003484942,0.02003895,0.002217043,0.0000912409,0.00006253527,0.000003692352,0.000001721438,0.00004494315],"genre_scores_gemma":[0.9442108,8.961793e-7,0.0555003,0.0002457578,0.00003308754,1.401447e-9,0.000002997023,0.0000021263,0.000004060382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1733398,"threshold_uncertainty_score":0.9911531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02887720945489376,"score_gpt":0.2131276972692585,"score_spread":0.1842504878143648,"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."}}