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Theory of Fine Sediment Infiltration into Immobile Gravel Bed

2008· article· en· W2046042371 on OpenAlexaff
Yantao Cui, John K. Wooster, Peter F. Baker, Scott R. Dusterhoff, L. S. Sklar, W. E. Dietrich

Bibliographic record

VenueJournal of Hydraulic Engineering · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsStillwater (Canada)
Fundersnot available
KeywordsInfiltration (HVAC)FlumeSedimentGeologySediment transportHydrology (agriculture)Soil scienceGeomorphologyGeotechnical engineeringFlow (mathematics)MechanicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

A theoretical model is developed to describe the process of fine sediment infiltration into immobile coarse sediment deposits. The governing equations are derived from mass conservation and the assumption that the amount of fine sediment deposition per unit vertical travel distance into the deposit is either constant or increases with increasing fine sediment fraction. Model results demonstrate that fine sediment accumulation decreases rapidly with depth into coarse substrate initially void of fine sediment, which is consistent with experimental observations that significant fine sediment infiltration occurs to only a shallow depth. Comparisons of the theory with flume data indicate that the model adequately reproduced the weighted-averaged fine sediment fraction values from experiments. An early model developed by Sakthivadivel and Einstein for fine sediment infiltration is in part based on the generally incorrect assumption that intragravel flow remains constant following fine sediment infiltration. Applying a correction to the Sakthivadivel and Einstein model based on alternate hypothesis that introgravel flow is driven by a constant head gives similar results as the proposed model.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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 designTheoretical or conceptual
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

Citations58
Published2008
Admission routes1
Has abstractyes

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