{"id":"W2017160174","doi":"10.1007/s00477-009-0325-z","title":"Mixed estimation methods for Halphen distributions with applications in extreme hydrologic events","year":2009,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computation; Estimator; Applied mathematics; Mathematics; Monte Carlo method; Extreme value theory; Independent and identically distributed random variables; Computational intelligence; Iterative method; Order statistic; Maximum likelihood; Algorithm; Mathematical optimization; Statistics; Computer science; Random variable","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001316524,0.0001605292,0.0001955948,0.0000954202,0.0005972018,0.00002286603,0.0001785433,0.00009051092,0.0001822025],"category_scores_gemma":[0.00005599828,0.0001228133,0.00004273348,0.0002851392,0.0004232996,0.0001646121,0.0001124735,0.0003253842,0.00002857886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003721123,"about_ca_system_score_gemma":0.00001655307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001122664,"about_ca_topic_score_gemma":0.0001247967,"domain_scores_codex":[0.9981635,0.0002837982,0.0002305044,0.000496805,0.0003379984,0.0004874014],"domain_scores_gemma":[0.999034,0.0004505281,0.00007650429,0.0002591122,0.000004637994,0.0001752314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004311059,0.003773589,0.4456186,0.00001609924,0.0001693339,0.000008472187,0.0004937837,0.08600356,0.006096215,0.005566028,0.0001435002,0.4516797],"study_design_scores_gemma":[0.001072983,0.001325356,0.5836989,0.00001078707,0.00008214412,0.000006291145,0.0002633887,0.2951623,0.0001718575,0.1174974,0.0004592595,0.0002493741],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3009467,0.00007699279,0.6975093,0.0002771725,0.000006937863,0.0008154252,0.00004261382,0.00001208686,0.0003127845],"genre_scores_gemma":[0.898009,0.0001007741,0.1010177,0.00001552896,0.00001130353,0.0005211418,0.0001734984,0.000007380185,0.0001437119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5970623,"threshold_uncertainty_score":0.5008183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05231901744866036,"score_gpt":0.38467244679451,"score_spread":0.3323534293458497,"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."}}