{"id":"W2123002812","doi":"10.1002/hyp.10550","title":"Assessing the performance of a semi‐distributed hydrological model under various watershed discretization schemes","year":2015,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Environment Canada","keywords":"Watershed; Discretization; Computer science; Representation (politics); Hydrological modelling; Structural basin; Hydrology (agriculture); Calibration; Distributed element model; Upstream (networking); Land cover; Drainage basin; Watershed management; Process (computing); Environmental science; Land use; Geology; Civil engineering; Machine learning; Mathematics; Geography; Statistics; Geomorphology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001329706,0.0004475795,0.0004192425,0.0003709934,0.0003845651,0.000942523,0.0007772782,0.0009606796,0.0007169313],"category_scores_gemma":[0.002994177,0.0002036299,0.0004604595,0.000434123,0.0005647961,0.0005917039,0.00055554,0.0005671511,0.00007748075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089127,"about_ca_system_score_gemma":0.001043242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03590229,"about_ca_topic_score_gemma":0.01712116,"domain_scores_codex":[0.9996942,0.0001119181,0.00003218371,0.00005480505,0.00006223875,0.00004469352],"domain_scores_gemma":[0.9979762,0.001245701,0.0002267322,0.0002138098,0.000252148,0.00008538063],"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":[0.00009391121,0.00004926711,0.004147208,0.00002249353,0.00002051586,0.00001894955,0.00002163207,0.9906798,0.001947141,0.0003596949,0.0000527427,0.00258663],"study_design_scores_gemma":[0.00001456699,0.00006438931,0.001315017,0.000005576648,0.000007555084,0.000004084226,0.0000215822,0.9972547,0.001119008,0.0001333273,0.00005437809,0.000006010389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744275,0.00009384999,0.02303411,0.0001142911,0.00001724353,0.00005968815,0.0002797737,0.0001934117,0.001780013],"genre_scores_gemma":[0.9932966,0.00002202815,0.006356139,0.00000796235,0.000001499584,0.00002370458,0.000107116,0.000007057131,0.0001778973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03590229,"threshold_uncertainty_score":0.07138664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857185251863944,"score_gpt":0.2680901378151981,"score_spread":0.2295182852965587,"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."}}