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Record W3000415349 · doi:10.1002/esp.4815

Width variations control the development of grain structuring in steep step‐pool dominated streams: insight from flume experiments

2020· article· en· W3000415349 on OpenAlexafffund
Matteo Saletti, Marwan A. Hassan

Bibliographic record

VenueEarth Surface Processes and Landforms · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsFlumeSTREAMSChannel (broadcasting)Deposition (geology)GeologyGeotechnical engineeringParticle (ecology)Hydrology (agriculture)Stress (linguistics)Shear stressGeomorphologyMechanicsComputer scienceSedimentFlow (mathematics)Physics

Abstract

fetched live from OpenAlex

ABSTRACT We report results from flume experiments designed to study the effect of width variations on the formation and stability of steps in steep streams. To physically model channel width changes we inserted multiple trapezoidal elements in the flume. Two competing effects are in play: a fluidic effect, suggesting that steps are more likely to form in wide areas because of deposition enhanced by lower shear stress, and a granular effect, suggesting that steps are more likely to form in narrow areas because of particle jamming. Our experiments show that width variations enhance the formation of steps. Although steps can form in every location, those in narrow/narrowing areas are more common, more stable and they occupy a larger portion of the channel width. These results stress the importance of particle interactions in coarse‐bedded streams and help river engineers by providing a new element to consider when designing step‐pool sequences in river restoration projects. © 2020 John Wiley & Sons, Ltd.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

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