{"id":"W2135916132","doi":"10.1016/j.jhydrol.2007.06.017","title":"Analysis and improvement of runoff generation in the land surface scheme CLASS and comparison with field measurements from China","year":2007,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; McGill University","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Interflow; Baseflow; Hydrograph; Surface runoff; Environmental science; Hydrology (agriculture); Streamflow; Sensible heat; Monsoon; Drainage basin; Climatology; Geology; Atmospheric sciences; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004617865,0.0003446249,0.0002803671,0.001372635,0.0004538238,0.0003585472,0.000403686,0.0002934842,0.0004459277],"category_scores_gemma":[0.0004325797,0.0002049172,0.000417668,0.002023938,0.0002909168,0.0004529683,0.0002234581,0.0001478562,0.0001330975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165987,"about_ca_system_score_gemma":0.001062123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05903649,"about_ca_topic_score_gemma":0.07041938,"domain_scores_codex":[0.9997608,0.00002495341,0.00001735616,0.00008641009,0.00006599738,0.00004436174],"domain_scores_gemma":[0.9996446,0.0000557905,0.00004991418,0.00004409744,0.0001584512,0.00004698739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001876422,0.0001612813,0.9426271,0.00004416369,0.00006830831,0.0001414333,0.0003876985,0.004550786,0.02027573,0.0001709558,0.0005106078,0.03087436],"study_design_scores_gemma":[0.000008341896,0.00002238912,0.9923775,0.00000132441,0.00002618203,0.00002982275,0.00007910036,0.005566908,0.001594931,0.00003382659,0.0002525554,0.00000705963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99893,0.00002620273,0.0003196173,0.0000100729,0.000001656599,0.000005173227,0.0003241055,0.00001733289,0.0003657152],"genre_scores_gemma":[0.9990006,0.00002304137,0.0002693733,0.000005623878,0.000002462699,0.000004972938,0.0005102679,0.000003679267,0.0001799316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05903649,"threshold_uncertainty_score":0.1173857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588036535360358,"score_gpt":0.2432361978782104,"score_spread":0.2273558325246068,"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."}}