{"id":"W2518515503","doi":"10.1002/2016wr018603","title":"A novel method to estimate the maximization ratio of the <scp>P</scp>robable <scp>M</scp>aximum <scp>P</scp>recipitation (<scp>P</scp>MP) 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009806822,0.000799241,0.0008137965,0.000525178,0.001924522,0.0006332927,0.002523584,0.0005429045,0.00005750872],"category_scores_gemma":[0.006280941,0.0004602469,0.0004282081,0.001672532,0.001353525,0.001082373,0.003165068,0.0009814096,0.0005768695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000877268,"about_ca_system_score_gemma":0.0001523515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164163,"about_ca_topic_score_gemma":0.0003586345,"domain_scores_codex":[0.9883841,0.001947191,0.001433624,0.001857205,0.003533279,0.002844651],"domain_scores_gemma":[0.988679,0.007663047,0.0005106241,0.002070824,0.0004504083,0.0006261005],"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":[0.00002767716,0.0004502472,0.003772496,0.0001744913,0.00009229494,0.00000603383,0.02964417,0.6338555,0.3223162,0.0007404197,0.008038964,0.0008815085],"study_design_scores_gemma":[0.001806341,0.0003095519,0.003356114,0.0003933765,0.0001339024,0.00006678268,0.004813762,0.8375908,0.06385236,0.01171539,0.07581364,0.0001479911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.849421,0.00009587267,0.1373875,0.00112225,0.0002027978,0.002292231,0.0002377352,0.0001452043,0.009095486],"genre_scores_gemma":[0.8668627,0.0003070724,0.07980226,0.001216387,0.0005659804,0.000908181,0.0001976176,0.0004825435,0.04965722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2584639,"threshold_uncertainty_score":0.9997849,"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."}}