{"id":"W3094353417","doi":"","title":"Memory effects of depressional storage in Northern Prairie hydrology","year":2010,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wetland; Hydrology (agriculture); Hydrography; Surface runoff; Environmental science; Drainage basin; STREAMS; Structural basin; Digital elevation model; Water storage; Drainage; Ephemeral key; Geology; Geography; Ecology; Geomorphology; Oceanography; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000505697,0.0001887179,0.000226696,0.0002387358,0.0003307664,0.0005968226,0.0004014935,0.0003988352,0.00065166],"category_scores_gemma":[0.003077742,0.0002378252,0.0003178886,0.0001800731,0.0006141854,0.0006181517,0.0003809344,0.0003122092,0.00004571187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008514824,"about_ca_system_score_gemma":0.0004990337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02158337,"about_ca_topic_score_gemma":0.0186571,"domain_scores_codex":[0.9998966,0.00002761496,0.000007729965,0.00002450352,0.00001037929,0.00003306863],"domain_scores_gemma":[0.9991535,0.0004763379,0.0001147847,0.0001005174,0.00006844399,0.00008632532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000617809,0.0003769959,0.2565848,0.00007371284,0.0001876749,0.0004147571,0.0007417279,0.7087233,0.01813207,0.00213217,0.0004006104,0.01161439],"study_design_scores_gemma":[0.00006045419,0.0003001965,0.1381546,0.00001611536,0.00006537991,0.00009518491,0.0003436175,0.8555642,0.003909871,0.001265202,0.0001903975,0.00003470711],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994935,0.00001039058,0.0001996534,0.00002054222,7.418067e-7,0.000002175514,0.00002230201,0.000009467809,0.0002412967],"genre_scores_gemma":[0.9998953,0.000005655178,0.00005292954,0.000002692895,3.481488e-7,8.423511e-7,0.00000855044,0.000001390017,0.00003215202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02158337,"threshold_uncertainty_score":0.04291552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005571667228290635,"score_gpt":0.213369524173445,"score_spread":0.2077978569451544,"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."}}