{"id":"W2563253922","doi":"10.1016/j.jhydrol.2016.12.025","title":"Flood frequency analysis using multi-objective optimization based interval estimation approach","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Quantile; Interval (graph theory); Prediction interval; Flood myth; Statistics; Interval estimation; Computer science; Mathematical optimization; Mathematics; Confidence interval","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006449143,0.0001276755,0.000376228,0.0003480923,0.00009603384,0.000009526598,0.0002155252,0.0001545517,0.001177521],"category_scores_gemma":[0.0001566477,0.00008423083,0.0002833956,0.0006469644,0.0002032484,0.0004017939,0.00004493765,0.0001431452,0.00003454107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001923877,"about_ca_system_score_gemma":0.00002607852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007302665,"about_ca_topic_score_gemma":0.00005247598,"domain_scores_codex":[0.9985856,0.0003054747,0.0004831157,0.0002114423,0.0002033101,0.0002110209],"domain_scores_gemma":[0.9990865,0.00009544256,0.0005244801,0.0001701606,0.00003708824,0.0000863001],"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":[0.00005085642,0.0001392003,0.1045227,0.000001087718,0.0003537369,0.0000143385,0.0001020294,0.8918944,0.002485601,0.000006856061,0.000008937277,0.0004202801],"study_design_scores_gemma":[0.0007973001,0.0001637735,0.009129109,0.000003942513,0.00105833,0.00006034621,0.00001269619,0.9879571,0.0004447263,0.0002637811,0.000007892685,0.000100966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3824914,0.00001749568,0.6168374,0.0001920624,0.00004351114,0.00003248392,0.000001514448,0.000009080965,0.0003750323],"genre_scores_gemma":[0.8357375,0.000004265545,0.1640474,0.0001471262,0.00002912239,0.000001471218,0.000002634223,0.000008973761,0.00002159558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.453246,"threshold_uncertainty_score":0.9997355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477584134883782,"score_gpt":0.2548497358285085,"score_spread":0.2400738944796707,"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."}}