{"id":"W2550231465","doi":"10.1061/(asce)wr.1943-5452.0000725","title":"Mapping Extreme Rainfall Statistics for Canada under Climate Change Using Updated Intensity-Duration-Frequency Curves","year":2016,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Chiropractic Association; Western University","funders":"Canadian Water Network","keywords":"Downscaling; Precipitation; Environmental science; Climatology; Climate change; Quantile; Extreme value theory; Baseline (sea); Intensity (physics); Statistics; Meteorology; Mathematics; Geography; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005979261,0.0001150961,0.0001768244,0.00006016253,0.0001616091,0.00003831453,0.0001194075,0.00002538063,0.00008979297],"category_scores_gemma":[0.00001357925,0.00006811803,0.0000282777,0.00004237926,0.00005225497,0.0002062367,0.0001475684,0.0000530438,0.00000143125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001680357,"about_ca_system_score_gemma":0.00000519774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006781972,"about_ca_topic_score_gemma":0.003237432,"domain_scores_codex":[0.9989147,0.0000348244,0.0003824007,0.0001529041,0.0002361323,0.0002790058],"domain_scores_gemma":[0.9995485,0.00004499646,0.0001906144,0.00009968712,0.00002993895,0.00008626694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002454641,0.0008399079,0.5618118,0.007453206,0.002481029,0.001905885,0.06320707,0.03921412,0.1136641,0.003620721,0.1561855,0.04716201],"study_design_scores_gemma":[0.01283898,0.001381212,0.232421,0.01430235,0.001454294,0.0008679784,0.01342171,0.05741103,0.003162097,0.02816212,0.6309332,0.003643978],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473901,0.0001537702,0.04858143,0.002959082,0.0002026161,0.0003178371,0.00006117993,0.00001133575,0.0003225838],"genre_scores_gemma":[0.9826012,0.0005158387,0.01453753,0.001929889,0.0001103769,0.00001338334,0.00001919237,0.00002011422,0.0002524631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4747477,"threshold_uncertainty_score":0.999832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06328193113851797,"score_gpt":0.2525064058152137,"score_spread":0.1892244746766957,"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."}}