{"id":"W7060795200","doi":"","title":"Open-channel flow through simulated vegetation : turbulence modeling and sediment transport","year":2016,"lang":"en","type":"other","venue":"US Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core)","topic":"Gyrotron and Vacuum Electronics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Coastal and Hydraulics Laboratory; U.S. Army Corps of Engineers; University of Illinois at Urbana-Champaign; Direktorat Riset and Pengembangan, Universitas Indonesia; Canada Excellence Research Chairs, Government of Canada","keywords":"Vegetation (pathology); Turbulence; Flow (mathematics); Sediment transport; Hydrology (agriculture); Vegetation cover; Sediment","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006244062,0.0005684448,0.0006597821,0.0006565299,0.0001406532,0.0001035575,0.0006536572,0.0002150583,0.000650243],"category_scores_gemma":[0.00001439588,0.0004837197,0.00007494989,0.0003093701,0.0001331452,0.0002095066,0.0004372941,0.0007114338,0.00008522224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001303672,"about_ca_system_score_gemma":0.0005202548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001365725,"about_ca_topic_score_gemma":0.0000340075,"domain_scores_codex":[0.9968389,0.00006141617,0.0005442018,0.0007948788,0.0006524128,0.001108134],"domain_scores_gemma":[0.9985763,0.0001229446,0.00009658998,0.0003989019,0.0004159816,0.0003892205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004138064,0.01812112,0.02670969,0.02665682,0.0221608,0.0003581267,0.06339412,0.1080502,0.006514405,0.08957956,0.4025155,0.2318016],"study_design_scores_gemma":[0.01336478,0.0007844154,0.0002671531,0.006414732,0.00009411895,0.00001086381,0.0005117214,0.4832612,0.00374289,0.002984172,0.4856396,0.002924391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03419206,0.03085558,0.5332873,0.0003129161,0.001577106,0.009780135,0.0008389589,0.0005764696,0.3885794],"genre_scores_gemma":[0.8926199,0.001576481,0.007034741,0.00001429231,0.0004047928,0.0002443984,0.0007643147,0.0006083226,0.09673274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8584279,"threshold_uncertainty_score":0.9997615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06371024289702695,"score_gpt":0.3414718077998885,"score_spread":0.2777615649028615,"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."}}