{"id":"W4408458392","doi":"10.5194/egusphere-2025-1076","title":"How well do hydrological models learn from limited discharge data? A comparison of process- and data-driven models","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Deutsche Forschungsgemeinschaft","keywords":"Process (computing); Computer science; Data science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01194851,0.0007998386,0.0009195029,0.001505952,0.0002571437,0.001664798,0.001480374,0.00192045,0.0005719984],"category_scores_gemma":[0.04520561,0.0005924445,0.001221431,0.001081407,0.0009213638,0.004204487,0.001050104,0.001066683,0.0002166271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374391,"about_ca_system_score_gemma":0.0009170434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005441809,"about_ca_topic_score_gemma":0.004082832,"domain_scores_codex":[0.99794,0.001235521,0.0001229699,0.0003317048,0.0002678144,0.0001019392],"domain_scores_gemma":[0.9643458,0.03029425,0.001491314,0.002071132,0.001373298,0.0004241616],"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":[0.00015276,0.00008378821,0.02407524,0.00007937699,0.0003008718,0.00004387742,0.00007779739,0.9568223,0.0004048236,0.001447859,0.0003031104,0.0162082],"study_design_scores_gemma":[0.0000171438,0.00004961319,0.007172385,0.00002090072,0.00002828069,0.00001449546,0.0000272453,0.9893768,0.0003785896,0.002738088,0.0001586936,0.00001781443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9051215,0.001246629,0.08786029,0.002123442,0.00006724674,0.00007836011,0.000660112,0.0004431869,0.002399312],"genre_scores_gemma":[0.9933124,0.000200092,0.005754594,0.00009752672,0.00002348864,0.00002831779,0.0003842261,0.00003798663,0.0001613646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01194851,"threshold_uncertainty_score":0.06319052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08484550785832427,"score_gpt":0.3070211194912585,"score_spread":0.2221756116329342,"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."}}