{"id":"W4386897831","doi":"10.1002/hyp.14995","title":"Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface","year":2023,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Catchment hydrology; Hydrology (agriculture); Hydrological modelling; Environmental science; Drainage basin; Process (computing); Water resource management; Geology; Computer science; Geography; Climatology; Geotechnical engineering; Cartography","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":[0.009687063,0.0007480832,0.0006871478,0.001504254,0.001015119,0.001828214,0.001744213,0.00171759,0.05713797],"category_scores_gemma":[0.04720916,0.0004348694,0.0008490304,0.002215498,0.0005469332,0.005352078,0.002737827,0.003337186,0.01952751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527826,"about_ca_system_score_gemma":0.002115054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01029509,"about_ca_topic_score_gemma":0.007467076,"domain_scores_codex":[0.9989706,0.0003420342,0.000121096,0.0001699646,0.0003472775,0.00004900296],"domain_scores_gemma":[0.9670432,0.00841505,0.0008450713,0.004352167,0.01816176,0.00118282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002399831,0.0001047315,0.003505481,0.001014684,0.00004221265,0.0003069149,0.0004693033,0.009246315,0.002735245,0.02986575,0.7905204,0.161949],"study_design_scores_gemma":[0.00009052668,0.000198412,0.007315549,0.00143113,0.00003314322,0.0002933325,0.0005465107,0.01788789,0.002450238,0.07303414,0.8966115,0.0001077032],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"editorial","genre_scores_codex":[0.01901441,0.01235615,0.4646054,0.1416972,0.1897912,0.003814449,0.08333741,0.003786184,0.0815976],"genre_scores_gemma":[0.186787,0.02569612,0.3988997,0.01742614,0.05826942,0.006553834,0.1364035,0.003536847,0.1664274],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.05713797,"threshold_uncertainty_score":0.1911455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05971664919413448,"score_gpt":0.3113747280049152,"score_spread":0.2516580788107807,"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."}}