{"id":"W4237385868","doi":"10.5194/gmd-2019-13","title":"The multiscale Routing Model mRM v1.0: simple river routing at resolutions from 1 to 50 km","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Bundesministerium für Bildung und Forschung; Agence Nationale de la Recherche","keywords":"Scalability; Parametrization (atmospheric modeling); Routing (electronic design automation); Streamflow; Computer science; Flow routing; Environmental science; Algorithm; Remote sensing; Geology; Drainage basin; Physics; Geography; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007044143,0.0009459318,0.0006374764,0.0005852997,0.0002230215,0.0008210334,0.002159342,0.0006485525,0.006211971],"category_scores_gemma":[0.00150375,0.0005099188,0.0009820898,0.0008485108,0.0002199855,0.0006755038,0.0006927635,0.001030875,0.003312531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005219484,"about_ca_system_score_gemma":0.0007970511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01453906,"about_ca_topic_score_gemma":0.01391928,"domain_scores_codex":[0.999724,0.00005198215,0.00002614908,0.00008511744,0.00007920731,0.00003361531],"domain_scores_gemma":[0.9996957,0.00007259821,0.00003149986,0.0001111998,0.00006510778,0.00002403176],"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":[0.0005877182,0.000375527,0.01012919,0.0008779092,0.0005563984,0.0003493367,0.0002580564,0.6727555,0.01804208,0.007588739,0.1685663,0.1199133],"study_design_scores_gemma":[0.0001915372,0.00008039317,0.006717102,0.00005682275,0.00005270232,0.000113831,0.00005990059,0.9280791,0.008548412,0.003003686,0.05301723,0.00007926569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.2346709,0.001089619,0.2247542,0.0009769372,0.0006299763,0.0008960397,0.3495134,0.1745701,0.01289877],"genre_scores_gemma":[0.3611713,0.0005514133,0.2657576,0.0003064129,0.00007574947,0.001189916,0.3592273,0.006323725,0.005396672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01453906,"threshold_uncertainty_score":0.02890885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257932235842628,"score_gpt":0.2475558937950697,"score_spread":0.2249765714366434,"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."}}