{"id":"W4390205540","doi":"10.1002/rra.4237","title":"Challenges in measuring fine sediment ingress in gravel‐bed rivers using retrievable sediment trap samplers","year":2023,"lang":"en","type":"article","venue":"River Research and Applications","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences; UK Research and Innovation","keywords":"Trap (plumbing); Sediment trap; Environmental science; Sediment; Hydrology (agriculture); Range (aeronautics); Flow (mathematics); Sediment transport; Soil science; Geology; Geotechnical engineering; Geomorphology; Environmental engineering; Materials science; Mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00111151,0.0001053288,0.0001354719,0.0002643546,0.0002442824,0.00001577116,0.0002105251,0.0000776063,0.0001631558],"category_scores_gemma":[0.00001691406,0.0001061387,0.00001758594,0.001167443,0.0003959497,0.0001603798,0.0001241572,0.0002882359,0.00009810935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710983,"about_ca_system_score_gemma":0.00002785887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008087672,"about_ca_topic_score_gemma":0.001285378,"domain_scores_codex":[0.998336,0.00007004685,0.0001934398,0.0004513894,0.0004333022,0.0005158887],"domain_scores_gemma":[0.9995074,0.0001302991,0.00002592038,0.00019343,0.00001436667,0.0001286102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003841885,0.001575028,0.7896412,0.0004210152,0.00009532812,0.0001762271,0.01670671,0.03018977,0.05303729,0.005865072,0.0007146762,0.1011935],"study_design_scores_gemma":[0.004574497,0.0004166739,0.8555186,0.0002871797,0.00002837168,0.00001222166,0.003231785,0.01253658,0.02587055,0.04619299,0.05038694,0.0009435874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958883,0.0004646236,0.000113684,0.001137663,0.00001317927,0.0006434534,0.000009711567,0.00004035811,0.001689048],"genre_scores_gemma":[0.9975719,0.001748067,0.0003029735,0.0000194995,0.00001805412,0.0002244914,0.00001907938,0.000009978085,0.00008593851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1002499,"threshold_uncertainty_score":0.432821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.199313827855768,"score_gpt":0.3521198408521895,"score_spread":0.1528060129964215,"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."}}