{"id":"W4413075649","doi":"10.14796/jwmm.h556","title":"Prediction of Streamflow in the Brahmani River using GEP, SVM, and MLR Models","year":2025,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Engineering Research Board; National Institute of Technology Rourkela; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Streamflow; Mean squared error; Support vector machine; Gene expression programming; Regression; Linear regression; Coefficient of determination; Environmental science; Statistics; Mathematics; Computer science; Machine learning; Geography; Drainage basin","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006087738,0.000599072,0.0005869668,0.0005046132,0.0001851288,0.0007503107,0.0006085716,0.000493761,0.0003216039],"category_scores_gemma":[0.001301878,0.0002114115,0.0006287444,0.0006446372,0.0002427362,0.0005327239,0.0002969925,0.0007180216,0.0001195897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589835,"about_ca_system_score_gemma":0.0006144189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007495,"about_ca_topic_score_gemma":0.00687775,"domain_scores_codex":[0.9997326,0.0000872919,0.00001796429,0.00007689375,0.00005019343,0.00003511198],"domain_scores_gemma":[0.9995353,0.0003135505,0.00005412381,0.00001824742,0.00006697353,0.00001183361],"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":[0.0000745217,0.00007606656,0.01586988,0.00006190416,0.0000596487,0.0001419007,0.00008311808,0.9354109,0.00334285,0.0008616413,0.0003494686,0.04366803],"study_design_scores_gemma":[0.000001634093,0.00001531216,0.001899431,0.000002606866,0.00000498292,0.00001112058,0.0000129724,0.9973021,0.000433964,0.0002322975,0.00007993403,0.000003741118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7299538,0.0005663275,0.2652383,0.000566233,0.00003864013,0.00005404937,0.0004223929,0.0009603844,0.002199849],"genre_scores_gemma":[0.9719829,0.0001858891,0.02644046,0.00003713009,0.00000955393,0.00004481385,0.0003250728,0.00002274009,0.0009515093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01007495,"threshold_uncertainty_score":0.02003258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04476898038590042,"score_gpt":0.2336420517622347,"score_spread":0.1888730713763343,"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."}}