{"id":"W1990673078","doi":"10.1007/s11269-006-9072-9","title":"Evaluation of three unit hydrograph models to predict the surface runoff from a Canadian watershed","year":2006,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Hydrograph; Watershed; Surface runoff; Hydrology (agriculture); Environmental science; Base flow; Predictability; Runoff model; Computer science; Geography; Statistics; Geology; Mathematics; Drainage basin; Cartography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001469574,0.0002069163,0.0001678022,0.0001110757,0.0003309505,0.00004394549,0.0005648936,0.00004976229,0.000831494],"category_scores_gemma":[0.000002048064,0.0001242765,0.00006987879,0.0002206986,0.0001979171,0.0001322648,0.0005726938,0.00007638049,0.0003959651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413747,"about_ca_system_score_gemma":0.000002388759,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.29014,"about_ca_topic_score_gemma":0.2231205,"domain_scores_codex":[0.9977047,0.0001855011,0.0002887516,0.0004274081,0.0008630093,0.0005306249],"domain_scores_gemma":[0.9992861,0.00001372062,0.00005106538,0.0005380761,0.00002005608,0.00009100931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00003718444,0.00006699124,0.06993014,0.00001007062,0.0003079865,0.00001114429,0.003894503,0.9173534,0.0002472369,0.000149703,0.005662113,0.00232955],"study_design_scores_gemma":[0.002550124,0.0002485274,0.4946509,0.00006421541,0.002073895,0.000001126269,0.001724262,0.1786689,0.00361586,0.09218904,0.2232063,0.00100687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9260409,0.00004613639,0.0004149971,0.002440352,0.00007671874,0.001045534,0.00001331066,0.00004150234,0.06988049],"genre_scores_gemma":[0.9977845,0.000006531925,0.0002776314,0.0003903695,0.00002970014,0.00009492924,0.0000450435,0.00001716541,0.001354115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7386845,"threshold_uncertainty_score":0.9104277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181456030192011,"score_gpt":0.2072426167847428,"score_spread":0.1854280564828227,"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."}}