{"id":"W4409707942","doi":"10.1007/s11269-025-04215-5","title":"Enhancing Rainfall-Runoff Simulation in Data-Poor Watersheds: Integrating Remote Sensing and Hybrid Decomposition for Hydrologic Modelling","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"Korea Environmental Industry and Technology Institute; Ministry of Education, India; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Environment; National Research Foundation","keywords":"Hydrogeology; Surface runoff; Environmental science; Hydrology (agriculture); Hydrological modelling; Decomposition; Remote sensing; Geology; Geotechnical engineering; Climatology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009678645,0.0002106658,0.0002222752,0.0001881858,0.000359558,0.00008193469,0.000255873,0.0000480111,0.00001836471],"category_scores_gemma":[0.00001097593,0.0001613019,0.00003252217,0.0001069145,0.00009850497,0.0002555134,0.001233276,0.000107781,0.00002062898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001036538,"about_ca_system_score_gemma":6.011269e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004474615,"about_ca_topic_score_gemma":0.0003322389,"domain_scores_codex":[0.9982952,0.00009156227,0.0003754217,0.0006615046,0.0001346029,0.0004417089],"domain_scores_gemma":[0.9994963,0.00007546006,0.00005978335,0.0003334275,0.000005607362,0.00002936463],"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":[0.0001644121,0.00002881447,0.001164398,0.0001852219,0.0001129448,0.00003042729,0.002443989,0.9646386,0.00126847,0.00003440813,0.0001652059,0.0297631],"study_design_scores_gemma":[0.0005943931,0.00003115104,0.0001464997,0.00009835374,0.00007870278,8.613948e-7,0.000322925,0.9745644,0.001663912,0.00499205,0.01732184,0.0001848591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5317031,0.00002793696,0.4639589,0.001113066,0.00007329977,0.0006066061,0.00000191736,0.00005571521,0.002459427],"genre_scores_gemma":[0.9772405,0.00003146147,0.02086882,0.0008402279,0.00002267253,0.000008600005,0.00008282724,0.0000131597,0.0008917449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4455374,"threshold_uncertainty_score":0.65777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761388160037719,"score_gpt":0.2636367824654965,"score_spread":0.2460229008651193,"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."}}