{"id":"W1980806603","doi":"10.1016/j.jhydrol.2006.01.016","title":"Improvement of rainfall-runoff forecasts through mean areal rainfall optimization","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Environmental science; Surface runoff; Rain gauge; Meteorology; Watershed; Sampling (signal processing); Hydrology (agriculture); Computer science; Filter (signal processing); Precipitation; Drainage basin; Geology; Geography; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007434716,0.0001800739,0.0004092286,0.00007618421,0.00008132211,0.00001301707,0.0003475552,0.0001672121,0.001126356],"category_scores_gemma":[0.0001128876,0.0001413447,0.0001706824,0.00021655,0.0003365305,0.00023573,0.000150914,0.0002606662,0.00002262598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556864,"about_ca_system_score_gemma":0.00002461195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000388245,"about_ca_topic_score_gemma":0.0001047552,"domain_scores_codex":[0.9980063,0.00009686021,0.0008762024,0.0002158861,0.0004198453,0.0003849239],"domain_scores_gemma":[0.9986697,0.0001053934,0.0009024956,0.000193724,0.00004748757,0.00008122691],"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":[0.0001387581,0.0002098977,0.004976367,0.000006403915,0.00003048242,0.0000464039,0.0002783331,0.9724299,0.01750492,0.00009303376,0.001942825,0.002342656],"study_design_scores_gemma":[0.010428,0.01572977,0.01837213,0.0001405757,0.0004754276,0.002491744,0.00007144162,0.799757,0.0317994,0.07531795,0.0439674,0.001449167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714041,0.00004172156,0.01886445,0.0009053174,0.0002883187,0.0001305923,0.00000356862,0.00001758274,0.008344384],"genre_scores_gemma":[0.9789285,0.00001375298,0.02018652,0.0005195079,0.0001739084,0.000002044704,0.000005165404,0.00001781843,0.0001527985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1726729,"threshold_uncertainty_score":0.9997867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000500431389701,"score_gpt":0.2233521878640251,"score_spread":0.2133471835501281,"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."}}