{"id":"W4413089651","doi":"10.20944/preprints202507.1195.v1","title":"A DeepAR-Based Modeling Framework for Probabilistic Mid-Long Term Streamflow Prediction","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China; University of Regina","keywords":"Probabilistic logic; Streamflow; Gamma distribution; Term (time); Computer science; Time horizon; Statistical model; Machine learning; Artificial intelligence; Statistics; Mathematics; Mathematical optimization; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007616621,0.0009700579,0.0006760973,0.0005971577,0.0003310797,0.001014136,0.001534025,0.0008682096,0.001461813],"category_scores_gemma":[0.001056351,0.000558171,0.001065051,0.0006587816,0.0003891189,0.001097446,0.0009409119,0.001566341,0.0004666786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007693615,"about_ca_system_score_gemma":0.001709299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02746303,"about_ca_topic_score_gemma":0.02320015,"domain_scores_codex":[0.9998065,0.00003882517,0.00001571593,0.00005920335,0.00005006529,0.00002976645],"domain_scores_gemma":[0.9997057,0.0001063891,0.00004244006,0.00001873182,0.0001033783,0.0000233634],"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":[0.0000120602,0.000012211,0.0003112275,0.00001461301,0.00002461248,0.00002899506,0.000009660997,0.9862465,0.000655721,0.002548054,0.0003149836,0.009821391],"study_design_scores_gemma":[8.083236e-7,0.000002718338,0.00003899906,0.000001300067,0.000002550826,0.000002524079,9.849206e-7,0.9991077,0.00007381909,0.0006641804,0.0001023493,0.000001980868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01649888,0.0003767858,0.9796788,0.0002226728,0.00005934337,0.00002598064,0.0004592495,0.001205611,0.001472701],"genre_scores_gemma":[0.786566,0.001060994,0.2055013,0.0002271992,0.0001691247,0.0002407674,0.002150194,0.0002462487,0.003838228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02746303,"threshold_uncertainty_score":0.05460632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08119430896076789,"score_gpt":0.3243770978459933,"score_spread":0.2431827888852254,"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."}}