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Record W2169105525 · doi:10.1029/2011jf002120

Using multiple bed load measurements: Toward the identification of bed dilation and contraction in gravel‐bed rivers

2011· article· en· W2169105525 on OpenAlexaffabout
G. A. Marquis, A. G. Roy

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBed loadGeologyBedformFluvialSediment transportFlood mythRiver bedGeotechnical engineeringSedimentHydrology (agriculture)Dilation (metric space)Stream bedEnvironmental scienceGeomorphologyGeometry

Abstract

fetched live from OpenAlex

This study examines bed load transport processes in a small gravel‐bed river (Béard Creek, Québec) using three complementary methods: bed elevation changes between successive floods, bed activity surveys using tags inserted into the bed, and bed load transport rates from bed load traps. The analysis of 20 flood events capable of mobilizing bed material led to the identification of divergent results among the methods. In particular, bed elevation changes were not consistent with the bed activity surveys. In many cases, bed elevation changes were significant (1 to 2 times the D 50 ) even if the bed surface had not been activated during the flood, leading to the identification of processes of bed dilation and contraction that occurred over 10% to 40% of the bed surface. These dynamics of the river bed prevent accurate derivation of bed load transport rates from topographic changes, especially for low magnitude floods. This paper discusses the mechanisms that could explain the dilation and contraction of particles within the bed and their implications in fluvial dynamics. Bed contraction seems to be the result of the winnowing of the fine sediments under very low gravel transport. Bed dilation seems to occur on patches of the bed at the threshold of motion where various processes such as fine sediment infiltration lead to the maintenance of a larger sediment framework volume. Both processes are also influenced by flood history and the initial local bed state and in turn may have a significant impact on sediment transport and morphological changes in gravel‐bed rivers.

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 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)0.002
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 score0.390
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.329
Teacher spread0.215 · 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 teacher head, 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

Citations34
Published2011
Admission routes2
Has abstractyes

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