{"id":"W4385318765","doi":"10.3390/atmos14081197","title":"Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach","year":2023,"lang":"en","type":"article","venue":"Atmosphere","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Mahasarakham University","keywords":"Artificial neural network; Estimation theory; Normalized Difference Vegetation Index; Computer science; Watershed; Statistics; Extreme value theory; Estimation; Environmental science; Meteorology; Mathematics; Climate change; Machine learning; Geography; Ecology","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":[0.002601976,0.0007885044,0.0008892853,0.001201974,0.0003060265,0.0009821384,0.00120533,0.001002129,0.0009393942],"category_scores_gemma":[0.008034016,0.000465667,0.0008950795,0.0009027885,0.0006968453,0.001407149,0.0009976403,0.001406079,0.0001397405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005765537,"about_ca_system_score_gemma":0.0006541518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004023456,"about_ca_topic_score_gemma":0.002708346,"domain_scores_codex":[0.9989702,0.0004671554,0.00008122544,0.0002378032,0.0001661288,0.00007758395],"domain_scores_gemma":[0.9970931,0.002131086,0.0003024573,0.0001066059,0.0003240303,0.00004269753],"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":[0.00002926373,0.00002619639,0.003359523,0.00004993314,0.00008878627,0.00007250471,0.00004720465,0.958438,0.000619514,0.005047189,0.0002225729,0.03199933],"study_design_scores_gemma":[0.00000161486,0.000005200126,0.0002832452,0.000004742899,0.000003429009,0.000008861754,0.000004581224,0.9971445,0.00008910207,0.002377473,0.00007297337,0.000004294744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01848031,0.0001829034,0.9804391,0.00009797108,0.00001530132,0.00002620746,0.00002802665,0.000114537,0.0006155544],"genre_scores_gemma":[0.8387821,0.0004881561,0.1585378,0.0001267908,0.00007318736,0.0002273662,0.0002762202,0.00005138522,0.001436828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004023456,"threshold_uncertainty_score":0.01376075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837049558405527,"score_gpt":0.2450195588508017,"score_spread":0.2066490632667464,"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."}}