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Record W1993795808 · doi:10.1029/2008jf001144

Response of bed surface patchiness to reductions in sediment supply

2009· article· en· W1993795808 on OpenAlexaff
Peter A. Nelson, Jeremy G. Venditti, W. E. Dietrich, James W. Kirchner, Hiroshi Ikeda, Fujiko Iseya, L. S. Sklar

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsSimon Fraser University
FundersNational Centre of Excellence in DesalinationNational Science Foundation
KeywordsBed loadSortingFlumeSedimentGeologyChannel (broadcasting)Flux (metallurgy)Grain sizeSediment transportHydrology (agriculture)BedformSoil scienceGeomorphologyGeotechnical engineeringMechanicsFlow (mathematics)Materials science

Abstract

fetched live from OpenAlex

River beds are often arranged into patches of similar grain size and sorting. Patches can be distinguished into “free patches,” which are zones of sorted material that move freely, such as bed load sheets; “forced patches,” which are areas of sorting forced by topographic controls; and “fixed patches” of bed material rendered immobile through localized coarsening that remain fairly persistent through time. Two sets of flume experiments (one using bimodal, sand‐rich sediment and the other using unimodal, sand‐free sediment) are used to explore how fixed and free patches respond to stepwise reductions in sediment supply. At high sediment supply, migrating bed load sheets formed even in unimodal, sand‐free sediment, yet grain interactions visibly played a central role in their formation. In both sets of experiments, reductions in supply led to the development of fixed coarse patches, which expanded at the expense of finer, more mobile patches, narrowing the zone of active bed load transport and leading to the eventual disappearance of migrating bed load sheets. Reductions in sediment supply decreased the migration rate of bed load sheets and increased the spacing between successive sheets. One‐dimensional morphodynamic models of river channel beds generally are not designed to capture the observed variability, but should be capable of capturing the time‐averaged character of the channel. When applied to our experiments, a 1‐D morphodynamic model (RTe‐bookAgDegNormGravMixPW.xls) predicted the bed load flux well, but overpredicted slope changes and was unable to predict the substantial variability in bed load flux (and load grain size) because of the migration of mobile patches. Our results suggest that (1) the distribution of free and fixed patches is primarily a function of sediment supply, (2) the dynamics of bed load sheets are primarily scaled by sediment supply, (3) channels with reduced sediment supply may inherently be unable to transport sediment uniformly across their width, and (4) cross‐stream variability in shear stress and grain size can produce potentially large errors in width‐averaged sediment flux calculations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.317
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations150
Published2009
Admission routes1
Has abstractyes

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