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Record W4213255831 · doi:10.1029/2021gl096088

The Impact of Intermittency on Bed Load Sediment Transport

2022· article· en· W4213255831 on OpenAlex
Santiago Benavides, Eric Deal, Matthew Rushlow, Jeremy G. Venditti, Qiong Zhang, Ken Kamrin, J. Taylor Perron

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsSimon Fraser University
FundersArmy Research LaboratoryNational Science Foundation
KeywordsIntermittencySediment transportEntrainment (biomusicology)SedimentBed loadGeologyEnvironmental scienceHydrology (agriculture)Soil scienceMechanicsGeotechnical engineeringTurbulenceGeomorphologyPhysics

Abstract

fetched live from OpenAlex

Abstract Sediment transport by wind or water near the threshold of grain motion is dominated by rare transport events. This intermittency makes it difficult to calibrate sediment transport laws, or to define an unambiguous threshold for grain entrainment, both of which are crucial for predicting sediment transport rates. We present a model that captures this intermittency and shows that the noisy statistics of sediment transport contain useful information about the sediment entrainment threshold and the variations in driving fluid stress. Using a combination of laboratory experiments and analytical results, we measure the threshold for grain entrainment in a novel way and introduce a new property, the “shear stress variability”, which predicts conditions under which transport will be intermittent. Our work suggests strategies for improving measurements and predictions of sediment flux and hints that the sediment transport law may change close to the threshold of motion.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.305
Teacher spread0.282 · 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