{"id":"W4282959946","doi":"10.1002/hyp.14635","title":"<scp>Spatio‐temporal</scp> discretization uncertainty of distributed hydrological models","year":2022,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; École de Technologie Supérieure","funders":"","keywords":"Discretization; Hydrological modelling; Temporal discretization; Scale (ratio); Temporal resolution; Environmental science; Uncertainty analysis; Spatial variability; Spatial ecology; Temporal scales; Calibration; Streamflow; Drainage basin; Hydrology (agriculture); Computer science; Mathematics; Statistics; Geology; Climatology; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002866942,0.0003243586,0.0004136788,0.0005709642,0.0003083134,0.001011767,0.0006841632,0.0004594377,0.0008200039],"category_scores_gemma":[0.01100773,0.0002307884,0.0005145466,0.0009135524,0.0005953077,0.0007948247,0.0005654441,0.0005784718,0.0000563891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563998,"about_ca_system_score_gemma":0.000965801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07096549,"about_ca_topic_score_gemma":0.04178239,"domain_scores_codex":[0.9991271,0.0004585859,0.00003622713,0.0001053117,0.0002137712,0.00005901931],"domain_scores_gemma":[0.9927126,0.005394307,0.0005260077,0.0007100308,0.0005745548,0.00008245775],"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":[0.00001464134,0.000008741158,0.004055036,0.00001186572,0.00004267414,0.00003170668,0.00001567881,0.9877323,0.0006520443,0.002560049,0.0002283101,0.004647068],"study_design_scores_gemma":[0.000001657018,0.000004071918,0.001950903,0.000005130376,0.000003508934,0.000006919403,0.00000620259,0.9957345,0.0004906812,0.001622786,0.0001688932,0.000004738379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4406161,0.000534781,0.5496155,0.0009897588,0.00006809899,0.00006429977,0.001923196,0.0007849924,0.005403276],"genre_scores_gemma":[0.9787602,0.0001218651,0.02020035,0.000042919,0.00001986229,0.00003251192,0.0004932914,0.0000475958,0.0002813845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07096549,"threshold_uncertainty_score":0.1411049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004691994608231,"score_gpt":0.2255544258936481,"score_spread":0.2055075059475658,"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."}}