{"id":"W4210738948","doi":"10.3390/w14030463","title":"Regression Tree Ensemble Rainfall–Runoff Forecasting Model and Its Application to Xiangxi River, China","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Ensemble forecasting; Artificial neural network; Ensemble learning; Decision tree; Linear regression; Regression; Regression analysis; Computer science; Random forest; Statistics; Data mining; Artificial intelligence; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005913641,0.0003835235,0.0006236251,0.0004496588,0.0003354496,0.0004424633,0.0005548346,0.0004813993,0.0007459159],"category_scores_gemma":[0.0009183602,0.0002036529,0.0005450742,0.0008370604,0.0001036749,0.0005953287,0.0003272182,0.0003897963,0.0000929869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004460623,"about_ca_system_score_gemma":0.0009087017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04088137,"about_ca_topic_score_gemma":0.02132188,"domain_scores_codex":[0.9998527,0.00003749736,0.00001086514,0.00003896462,0.00003574091,0.00002424707],"domain_scores_gemma":[0.9998267,0.00006697184,0.00002039501,0.00001221345,0.00006133778,0.00001237654],"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":[0.00002378984,0.00002536273,0.00289757,0.00001757115,0.00003359321,0.00005715051,0.0000236856,0.973436,0.0004949633,0.0005358699,0.0003115018,0.02214292],"study_design_scores_gemma":[0.000001053769,0.000005028507,0.0003248122,7.505393e-7,0.000004072212,0.000002379842,0.000002631453,0.9994552,0.00005190754,0.00009298964,0.00005784208,0.000001384015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7085792,0.00108819,0.283666,0.0004889526,0.00009521933,0.00004579487,0.0003643314,0.0006653907,0.00500702],"genre_scores_gemma":[0.9858071,0.0004151558,0.01213465,0.00002032481,0.00001785412,0.00003619705,0.0002041442,0.00001768918,0.00134692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04088137,"threshold_uncertainty_score":0.08128685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222797035704022,"score_gpt":0.2280380268121621,"score_spread":0.2058100564551219,"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."}}