{"id":"W3047209549","doi":"10.5194/esurf-2020-51","title":"Short communication: Multiscalar drag decomposition in fluvialsystems using a transform-roughness correlation (TRC) approach","year":2020,"lang":"en","type":"article","venue":"","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Drag; Surface finish; Bedform; Roughness length; Beach morphodynamics; Hydraulic roughness; Flow (mathematics); Surface roughness; Wavelength; Geology; Standard deviation; Geometry; Mechanics; Mathematics; Materials science; Optics; Meteorology; Physics; Statistics; Geomorphology; Sediment transport; Sediment; Composite material","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0006149479,0.0004112604,0.0003206976,0.0007437744,0.0002533403,0.0005628548,0.0003126525,0.0004398268,0.002179423],"category_scores_gemma":[0.001436381,0.0001756678,0.0003596269,0.0006296353,0.0004049171,0.0004631377,0.0003350469,0.0006525487,0.0004876084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003314799,"about_ca_system_score_gemma":0.0004083104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003562399,"about_ca_topic_score_gemma":0.004596909,"domain_scores_codex":[0.9998544,0.00003131889,0.000007317758,0.00003109954,0.00006308407,0.00001278948],"domain_scores_gemma":[0.9994666,0.0002027728,0.00007978859,0.00007715739,0.0001321788,0.00004155293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002321395,0.0002256239,0.01730059,0.0003141039,0.0001479647,0.0004490213,0.0002083453,0.4050704,0.09402168,0.03322767,0.01418807,0.4346144],"study_design_scores_gemma":[0.000006590559,0.00003164769,0.005262892,0.000005059193,0.000008060134,0.00005496657,0.00001342825,0.9870443,0.003675961,0.002385555,0.00149181,0.00001980324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1632788,0.0004513613,0.8308464,0.000366979,0.0002422941,0.00007003616,0.0005591163,0.00130854,0.002876524],"genre_scores_gemma":[0.6900858,0.0003115986,0.3052992,0.00009655988,0.0001872205,0.00006569432,0.0007139929,0.0002946364,0.002945205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003562399,"threshold_uncertainty_score":0.00729084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02642967190966292,"score_gpt":0.2613705363649327,"score_spread":0.2349408644552698,"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."}}