{"id":"W3112847184","doi":"10.5194/esurf-8-1039-2020","title":"Short communication: Multiscalar roughness length decomposition in fluvial systems using a transform-roughness correlation (TRC) approach","year":2020,"lang":"en","type":"article","venue":"Earth Surface Dynamics","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bedform; Surface finish; Beach morphodynamics; Geology; Channel (broadcasting); Roughness length; Wavelength; Hydraulics; Surface roughness; Hydraulic roughness; Geometry; Open-channel flow; Range (aeronautics); Flow (mathematics); Sediment transport; Materials science; Optics; Geomorphology; Mathematics; Sediment; Computer science; Engineering; Physics; Composite material; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003154455,0.0002036015,0.0002661955,0.00002377368,0.0002365246,0.00003899248,0.0002865849,0.0002046871,0.0000635508],"category_scores_gemma":[0.000007187487,0.0002165543,0.00005293102,0.0004124705,0.0001592021,0.000656812,0.00004405253,0.0003654948,0.00004277822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001171896,"about_ca_system_score_gemma":0.0000184547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003434999,"about_ca_topic_score_gemma":0.0004861003,"domain_scores_codex":[0.9984682,0.0001665421,0.000406888,0.0003800481,0.0002852557,0.0002931134],"domain_scores_gemma":[0.9995322,0.00005156504,0.00006936341,0.0002266772,0.00001342509,0.0001067871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007181178,0.0001124812,0.1773894,0.00004793101,0.00001090949,0.000006112085,0.001675127,0.8195025,0.0003404719,0.0001228448,0.000003605589,0.0007167687],"study_design_scores_gemma":[0.0004853561,0.0000383109,0.03939836,0.00002827494,0.00002720012,0.00001144457,0.0003255484,0.9591817,0.00008984833,0.00002715958,0.0001520466,0.0002346909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496685,0.0001417426,0.1474282,0.0002581768,0.0001008332,0.000483775,0.0000239657,0.0000752941,0.001819494],"genre_scores_gemma":[0.9939495,0.00005793318,0.005440807,0.00007940194,0.00001771869,0.00001283405,0.0003872963,0.00002261994,0.00003183299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.144281,"threshold_uncertainty_score":0.8830829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174775124430656,"score_gpt":0.2422071178726978,"score_spread":0.2247296054296322,"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."}}