{"id":"W2913897181","doi":"10.5194/gmd-12-2501-2019","title":"The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km","year":2019,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Bundesministerium für Bildung und Forschung; Agence Nationale de la Recherche","keywords":"Scalability; Streamflow; Routing (electronic design automation); Computer science; Environmental science; Flow routing; Remote sensing; Algorithm; Drainage basin; Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009154677,0.0002469168,0.0001938578,0.00005285609,0.002574192,0.00009236345,0.0005967508,0.00007461912,0.0003306805],"category_scores_gemma":[0.00004022982,0.0001904757,0.00006623119,0.0002363197,0.0002534445,0.0001878525,0.002534349,0.0001457147,0.005385855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005129057,"about_ca_system_score_gemma":0.00002785002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005122439,"about_ca_topic_score_gemma":0.001491026,"domain_scores_codex":[0.9972084,0.00005028447,0.0004204116,0.0009001326,0.0005529525,0.0008678152],"domain_scores_gemma":[0.9990501,0.00007699458,0.0001061518,0.0005982777,0.00001682624,0.0001516784],"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":[0.00003087963,0.00006307004,0.03775191,0.000003785442,0.00004574142,0.000001898541,0.01002737,0.9173117,0.002444113,0.0002847575,0.02822446,0.003810263],"study_design_scores_gemma":[0.0002742333,0.000007454143,0.02479235,0.00001255407,0.00001595156,3.970637e-7,0.0002096029,0.9176694,0.0004883641,0.00120036,0.05500993,0.0003194395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8992479,0.00001885351,0.09093339,0.0008730482,0.0005067806,0.0006141123,0.00005026672,0.00008891266,0.007666779],"genre_scores_gemma":[0.9165221,0.000007182938,0.02194636,0.0005178976,0.00001411219,0.00009037439,0.00007093009,0.00001834694,0.06081264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06898703,"threshold_uncertainty_score":0.9987243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01704150845635995,"score_gpt":0.2175468424433536,"score_spread":0.2005053339869937,"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."}}