{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000641642,0.0003629392,0.0002955931,0.0008595465,0.0002530948,0.0006452533,0.0003179459,0.0005297104,0.002032162],"category_scores_gemma":[0.002260986,0.000176073,0.0003763072,0.0007205294,0.0004243903,0.0005781105,0.0003894852,0.00066636,0.0005589038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003214883,"about_ca_system_score_gemma":0.0004042284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002918824,"about_ca_topic_score_gemma":0.003469935,"domain_scores_codex":[0.9998342,0.00004206735,0.000009288373,0.00003669389,0.00006380607,0.000013935],"domain_scores_gemma":[0.9992658,0.0003220714,0.0001037978,0.00008444421,0.0001768927,0.00004703471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001862787,0.0001486501,0.0164362,0.0003259617,0.0001292131,0.0003980398,0.000191755,0.4136276,0.06431829,0.03126908,0.01403184,0.4589371],"study_design_scores_gemma":[0.000005858102,0.00002587648,0.004758878,0.000006858449,0.000009371046,0.00005998141,0.00001672444,0.9866158,0.002550355,0.004173163,0.001757324,0.00001982523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1136675,0.000575843,0.8804893,0.0004598068,0.0002295418,0.00006719035,0.0005540823,0.0009286706,0.00302802],"genre_scores_gemma":[0.6871412,0.0004200804,0.3081903,0.0001124656,0.0002711643,0.00008153592,0.0009092413,0.0002512444,0.002622603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002918824,"threshold_uncertainty_score":0.006798267,"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."}}