{"id":"W1705083953","doi":"10.1029/2006wr005044","title":"Spatial‐scale partitioning of in situ turbulent flow data over a pebble cluster in a gravel‐bed river","year":2007,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Turbulence; Spatial variability; Flow (mathematics); Reynolds number; Spatial ecology; Reynolds stress; Mathematics; Turbulence kinetic energy; Geometry; Scale (ratio); Geology; Physics; Mechanics; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003188209,0.0001078689,0.0001793544,0.0002162579,0.00009067362,0.00001920694,0.0006026987,0.0001156833,0.002158545],"category_scores_gemma":[0.00002815329,0.00008151244,0.00002408381,0.000355542,0.0005022074,0.0002499721,0.0004786488,0.000401571,0.0002108491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008858142,"about_ca_system_score_gemma":0.000008502674,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005834301,"about_ca_topic_score_gemma":0.0301055,"domain_scores_codex":[0.9976252,0.0001762131,0.000358737,0.000460256,0.0006791882,0.0007004358],"domain_scores_gemma":[0.9993271,0.00009898187,0.00002555519,0.000443004,0.00001371212,0.00009167651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005416322,0.0003734409,0.943849,0.0000624973,0.00001002363,0.0001688083,0.02640532,0.004264412,0.02204685,0.000001817602,0.0003576804,0.001918552],"study_design_scores_gemma":[0.002448123,0.0002370747,0.8284942,0.0001416373,0.000009697303,0.000009737155,0.0004775226,0.01585845,0.1138769,0.001001224,0.03710691,0.0003385389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963701,0.00004885326,0.0002125768,0.000477803,0.00001653772,0.0002604785,0.000006162613,0.00001004071,0.002597426],"genre_scores_gemma":[0.9990776,0.00001868102,0.0002874116,0.00008689834,0.00002667936,0.00001466822,0.0000714078,0.00001150193,0.0004051867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1153548,"threshold_uncertainty_score":0.9987536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938740156230284,"score_gpt":0.3078896228491508,"score_spread":0.2685022212868479,"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."}}