{"id":"W2518515503","doi":"10.1002/2016wr018603","title":"A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output","year":2016,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Climate variability and models","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upper and lower bounds; Maximization; Limiting; Limit (mathematics); Value (mathematics); Precipitable water; Expectation–maximization algorithm; Series (stratigraphy); Precipitation; Environmental science; Mathematics; Meteorology; Statistics; Mathematical optimization; Maximum likelihood; Physics; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006963587,0.0005086387,0.000405793,0.001070081,0.0001945325,0.000477587,0.0006223429,0.0003380297,0.0008927079],"category_scores_gemma":[0.002403551,0.0003113404,0.0004037141,0.0005452582,0.0002130279,0.0005291311,0.000463039,0.0004021431,0.0002571498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004657413,"about_ca_system_score_gemma":0.000721307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005740293,"about_ca_topic_score_gemma":0.007270775,"domain_scores_codex":[0.9996512,0.00007075917,0.00002861547,0.0001185237,0.0001069976,0.00002386625],"domain_scores_gemma":[0.999424,0.0002061856,0.0001316304,0.0000474464,0.0001688698,0.00002190783],"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":[0.0004413589,0.0002415091,0.03507202,0.0002177957,0.0003263629,0.0001448976,0.000139429,0.21262,0.1174253,0.003666307,0.001621581,0.6280833],"study_design_scores_gemma":[0.0000211341,0.00004627117,0.01047201,0.000008538737,0.0000286316,0.00008506117,0.0000158473,0.970768,0.01721998,0.0004236393,0.0008865906,0.0000242873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07326367,0.00009640321,0.9246496,0.00005205589,0.00001972078,0.00006030745,0.0001394848,0.0009591653,0.0007596314],"genre_scores_gemma":[0.4518618,0.00007766844,0.5467609,0.00002668936,0.00002765105,0.0001088264,0.0002772132,0.00008268062,0.0007766604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005740293,"threshold_uncertainty_score":0.01141375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08990949888295459,"score_gpt":0.3495407816042282,"score_spread":0.2596312827212736,"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."}}