{"id":"W3010746650","doi":"10.5194/nhess-20-1919-2020","title":"Sensitivity and identifiability of rheological parameters in debris flow modeling","year":2020,"lang":"en","type":"article","venue":"Natural hazards and earth system sciences","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Division of Mathematical Sciences; Comisión Nacional de Investigación Científica y Tecnológica; Facultad de Ciencias Físicas y Matemáticas; Centro Avanzado de Tecnología para la Minería; Universidad de Chile","keywords":"Identifiability; Sensitivity (control systems); Debris flow; Flood myth; Flow (mathematics); Environmental science; Rheology; Equifinality; Geology; Debris; Computer science; Hydrology (agriculture); Mathematics; Geotechnical engineering; Statistics; Meteorology; Geography; Engineering; Geometry; 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":[0.0009400853,0.0001033069,0.0002355739,0.00002321859,0.0001355759,0.00004752774,0.00008360185,0.00008109654,0.00001573688],"category_scores_gemma":[0.00007145652,0.00006407683,0.00003856368,0.0002883358,0.0004764106,0.0002182969,0.0001546235,0.000150106,0.000004995294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001407544,"about_ca_system_score_gemma":0.00001179027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008926787,"about_ca_topic_score_gemma":0.000221752,"domain_scores_codex":[0.998733,0.000126041,0.000238725,0.0003883649,0.0003039646,0.000209882],"domain_scores_gemma":[0.9996856,0.00007465182,0.00005517259,0.00006642527,0.000008746902,0.0001093324],"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":[0.0001929802,0.00006008195,0.5574859,0.0003662542,0.00002852954,0.00009895585,0.003144467,0.2519918,0.009553717,0.0006476442,0.00006291644,0.1763668],"study_design_scores_gemma":[0.0001619731,0.00007368639,0.03090944,0.00004118226,0.000005934985,0.00002404609,0.0005394411,0.9677909,0.0002974137,0.00004988022,0.00000904946,0.00009705355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982979,0.0003630838,0.0002443181,0.0003446919,0.0001022381,0.0001293067,0.000005106521,0.00001964635,0.0004936702],"genre_scores_gemma":[0.9980878,0.00004021782,0.001789267,0.00006084669,0.00001053558,0.000001223751,5.775543e-7,0.000002077467,0.000007466499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7157992,"threshold_uncertainty_score":0.2612977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589945313493513,"score_gpt":0.2233773723390736,"score_spread":0.2074779192041385,"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."}}