{"id":"W4388201889","doi":"10.18280/mmep.100521","title":"Hybrid Algorithm of Backpropagation and Relevance Vector Machine with Radial Basis Function Kernel for Hydro-Climatological Data Prediction","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Backpropagation; Radial basis function; Computer science; Artificial neural network; Relevance vector machine; Machine learning; Algorithm; Artificial intelligence; Evapotranspiration; Mean squared error; Support vector machine; Kernel (algebra); Wind speed; Data mining; Mathematics; Statistics; Meteorology","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":[0.001855662,0.001253205,0.00118783,0.001016299,0.0004400352,0.0008129625,0.001548941,0.001313326,0.00108845],"category_scores_gemma":[0.003543333,0.0004579375,0.0008355333,0.001035649,0.0003830668,0.001286824,0.000720483,0.00116563,0.0007313461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005825729,"about_ca_system_score_gemma":0.001295437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009571628,"about_ca_topic_score_gemma":0.005215371,"domain_scores_codex":[0.998936,0.0002495945,0.00009480599,0.0002296822,0.0003760845,0.0001138966],"domain_scores_gemma":[0.9990183,0.0003133067,0.00007938779,0.00009391962,0.0004682408,0.00002670253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002370983,0.0001885159,0.001823266,0.000156571,0.0001685528,0.0001423175,0.0001129754,0.4028167,0.00978862,0.002263764,0.002338051,0.5799636],"study_design_scores_gemma":[0.000008421028,0.00004896841,0.000349544,0.000004565061,0.00001108154,0.00002829977,0.00000589238,0.996615,0.001990375,0.0004059849,0.0005226776,0.000009065082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0386756,0.001009905,0.955833,0.000167811,0.0001125265,0.0001031172,0.00005359865,0.002463636,0.001580966],"genre_scores_gemma":[0.5847431,0.0007175037,0.4073988,0.0001470887,0.00008899454,0.0003242371,0.0002861001,0.0001522059,0.006141953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009571628,"threshold_uncertainty_score":0.01903188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03385867687077909,"score_gpt":0.2114484017603413,"score_spread":0.1775897248895622,"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."}}