{"id":"W4368353648","doi":"10.1016/j.ejrh.2023.101407","title":"Regional flood frequency analysis based on peaks-over-threshold approach: A case study for South-Eastern Australia","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrology Regional Studies","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Flood myth; Ordinary least squares; Range (aeronautics); Estimation; Statistics; Surface runoff; Environmental science; Hydrology (agriculture); Computer science; Econometrics; Geography; Mathematics; Geology; Engineering; Ecology; Geotechnical engineering","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":[0.0009550806,0.0002884287,0.0003131963,0.0008714541,0.0004246417,0.0005183247,0.0004675513,0.0003561565,0.0007382227],"category_scores_gemma":[0.002344954,0.0001678333,0.0004394831,0.001260832,0.000303796,0.000456389,0.0005912661,0.0003187256,0.0001038515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167279,"about_ca_system_score_gemma":0.0007357642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1012413,"about_ca_topic_score_gemma":0.1589737,"domain_scores_codex":[0.9996258,0.0001745156,0.00002989174,0.00005604504,0.00007114494,0.00004253841],"domain_scores_gemma":[0.9989805,0.0004707569,0.0001420686,0.00008586457,0.0002516588,0.00006923391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003386023,0.0008284923,0.6296197,0.0005480064,0.0003104486,0.01470746,0.01170629,0.1441292,0.013829,0.005452469,0.002555557,0.1759749],"study_design_scores_gemma":[0.00003347957,0.0003783063,0.7332802,0.00008620932,0.0001487413,0.001174454,0.008609738,0.2478604,0.00261498,0.001473552,0.004266662,0.0000732028],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937056,0.00007176693,0.004388866,0.00008930235,0.000002447768,0.0000670503,0.0001210269,0.00002201129,0.001531934],"genre_scores_gemma":[0.9907997,0.000135059,0.00786733,0.00001361428,0.000004097631,0.000031848,0.0001338947,0.000008914339,0.001005479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1012413,"threshold_uncertainty_score":0.201304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065921652179029,"score_gpt":0.338945374912667,"score_spread":0.2323532096947641,"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."}}