{"id":"W2169495306","doi":"10.1002/2013wr013857","title":"Improving process representation in conceptual hydrological model calibration using climate simulations","year":2014,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Hydro-Québec","funders":"","keywords":"Hydrometeorology; Representation (politics); Evapotranspiration; Calibration; Process (computing); Computer science; Hydrological modelling; Environmental science; Water cycle; Climate model; Snow; Climate change; Hydrology (agriculture); Meteorology; Precipitation; Climatology; Mathematics; Statistics; Geography; Ecology; Geology","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":[0.001190918,0.0001025384,0.0001319105,0.0001357332,0.0004853742,0.00005526308,0.0002198378,0.00009016668,0.0002406286],"category_scores_gemma":[0.0001054344,0.00007334592,0.00002344487,0.0002467002,0.000537167,0.0003462857,0.000536008,0.00025796,0.00008184287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008023383,"about_ca_system_score_gemma":0.00000225243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004527845,"about_ca_topic_score_gemma":0.0001807142,"domain_scores_codex":[0.9978808,0.000420667,0.0002323613,0.0004249531,0.0004389895,0.0006022226],"domain_scores_gemma":[0.9996137,0.00009113969,0.00003052847,0.0001949906,0.00001366876,0.00005595288],"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":[0.0000555187,0.00004042287,0.1041222,0.0000100254,0.000003518826,0.000003307659,0.006669079,0.8749609,0.01380377,0.00005710002,0.00001643725,0.0002577483],"study_design_scores_gemma":[0.0003046033,0.00005514543,0.002120339,0.000005103871,0.000004037633,6.680053e-7,0.0003448767,0.9904403,0.003782947,0.002643316,0.0002009687,0.000097672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935148,0.000003908601,0.002847206,0.0004447646,0.00001505941,0.0003045769,0.000001744776,0.00003648353,0.002831396],"genre_scores_gemma":[0.9993115,0.000002726281,0.0002898092,0.0001006524,0.00003519965,0.00003226071,0.00001591363,0.00001101174,0.0002009102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1154794,"threshold_uncertainty_score":0.3733155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0801450580802011,"score_gpt":0.3512497236399258,"score_spread":0.2711046655597247,"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."}}