{"id":"W4366281264","doi":"10.3390/hydrology10040095","title":"A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Streamflow; Regression; Random forest; Computer science; Boosting (machine learning); Model selection; Gradient boosting; AdaBoost; Time series; Machine learning; Calibration; Ensemble learning; Regression analysis; Decision tree; Artificial intelligence; Ensemble forecasting; Predictive modelling; Data mining; Statistics; Mathematics; Geography; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002034328,0.001319882,0.001169461,0.001301151,0.0004567141,0.0009019,0.001538309,0.001041676,0.0008403535],"category_scores_gemma":[0.003028025,0.0004328437,0.001285924,0.001412417,0.0003454282,0.001191077,0.0005593984,0.00170935,0.0003973108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008505041,"about_ca_system_score_gemma":0.00148995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01829039,"about_ca_topic_score_gemma":0.01711663,"domain_scores_codex":[0.9993638,0.0002321568,0.00004896469,0.0001531739,0.0001550911,0.0000467178],"domain_scores_gemma":[0.9992931,0.000372219,0.0001109514,0.00004942445,0.0001498968,0.00002449428],"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":[0.0000160036,0.00004801239,0.0009912709,0.00005208312,0.00007831339,0.00003668061,0.000025759,0.9475807,0.0005972649,0.008171764,0.0009495222,0.04145249],"study_design_scores_gemma":[0.000001270061,0.00001002277,0.0001353694,0.000004438244,0.000005414098,0.000006066545,0.000001800873,0.9971362,0.00008858601,0.002136913,0.0004699658,0.00000383203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005245541,0.000566589,0.9926675,0.0001464512,0.00003175153,0.00003172096,0.0002098284,0.0006546555,0.0004459569],"genre_scores_gemma":[0.3908171,0.001602181,0.6031916,0.0001550632,0.0002511542,0.0004728897,0.001417595,0.0001150814,0.001977307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01829039,"threshold_uncertainty_score":0.03636789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032860140204604,"score_gpt":0.2221916588030081,"score_spread":0.2118630574009621,"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."}}