{"id":"W1720026832","doi":"10.1029/2010wr009851","title":"Identification of nonlinearity in rainfall‐flow response using data‐based mechanistic modeling","year":2011,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Engineering and Physical Sciences Research Council","keywords":"Nonlinear system; Flow (mathematics); Conceptual model; Routing (electronic design automation); Subsurface flow; Calibration; Linear model; Environmental science; Variance (accounting); Flow routing; Computer science; Mathematics; Mechanics; Statistics; Geology; Geotechnical engineering; Physics","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":[0.003464772,0.0001167031,0.0001701609,0.0005656115,0.0000813778,0.0000508486,0.0005999148,0.0001266312,0.00005331937],"category_scores_gemma":[0.000192522,0.00009643144,0.00002789032,0.0004258571,0.00006918851,0.0001722861,0.0001735337,0.0004361219,0.00003044516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007768865,"about_ca_system_score_gemma":0.00003023364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006953903,"about_ca_topic_score_gemma":0.00009095275,"domain_scores_codex":[0.9980412,0.0003460825,0.0004067037,0.0002900567,0.0004609956,0.0004549817],"domain_scores_gemma":[0.9989111,0.00009965176,0.00001727549,0.0007484113,0.0001497783,0.00007372472],"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":[0.0009320121,0.00009581621,0.0003557321,0.0001963322,0.00002684827,0.00004169483,0.006245917,0.5330189,0.4579089,0.000008480411,0.00001521614,0.001154099],"study_design_scores_gemma":[0.0002812457,0.00002228376,0.0001826655,0.00005329215,0.00000520395,0.000001707258,0.0001885359,0.9100607,0.08875605,0.0001984165,0.0001450346,0.0001048908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883314,0.00008341498,0.01106388,0.00001879345,0.00006631817,0.0001804943,0.00004085948,0.00007222134,0.0001426654],"genre_scores_gemma":[0.9970401,0.000007814672,0.002767544,0.000003732763,0.00003987458,0.00001011906,0.00005333878,0.00003495259,0.00004250121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3770417,"threshold_uncertainty_score":0.393236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1911141934731247,"score_gpt":0.3409513409302528,"score_spread":0.1498371474571281,"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."}}