{"id":"W4281618214","doi":"10.1002/hyp.14618","title":"Assessing the efficacy of offline water storage ponds for natural flood management","year":2022,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; Natural Environment Research Council; University of Bristol; Natural England","keywords":"Environmental science; Hydrology (agriculture); Hydrograph; Flood myth; Surface runoff; Storm; Channel (broadcasting); Retention basin; Stream restoration; Digital elevation model; Flooding (psychology); STREAMS; Stormwater; Geography; Remote sensing; Geology; Computer science; Ecology; Meteorology","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":[0.0005924313,0.0001539796,0.000173593,0.00003405467,0.0005201146,0.00005320991,0.0005779877,0.00002538441,0.001822141],"category_scores_gemma":[0.00003754464,0.00008122435,0.00008095157,0.000263829,0.0001624446,0.000198841,0.00106289,0.0001675209,0.00002152803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000642016,"about_ca_system_score_gemma":0.000005879629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001172344,"about_ca_topic_score_gemma":0.000005462216,"domain_scores_codex":[0.9985009,0.00007708321,0.0002558313,0.0003596854,0.0004550097,0.0003515255],"domain_scores_gemma":[0.9994811,0.0001451409,0.00009525257,0.0002328673,0.00001080095,0.00003486704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001957938,0.01135218,0.02453935,0.002326196,0.001114444,0.0002792878,0.004971514,0.7451194,0.05939588,0.008738151,0.06493863,0.07526704],"study_design_scores_gemma":[0.01055323,0.003890165,0.09448824,0.0000545496,0.0009725328,0.00004394466,0.004608665,0.09614774,0.02550412,0.02473772,0.7368278,0.002171278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846765,0.0001981818,0.002196235,0.001981031,0.0002318914,0.0009035845,0.00000573674,0.00007646532,0.009730383],"genre_scores_gemma":[0.9963732,0.00002094117,0.00115157,0.0006247489,0.00004110059,0.0002959748,0.00003832365,0.00001256077,0.001441541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6718892,"threshold_uncertainty_score":0.9990903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674642272940606,"score_gpt":0.2741822449493669,"score_spread":0.2574358222199608,"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."}}