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Record W2770296624 · doi:10.1680/jwama.17.00039

Non-uniform flow over cobble bed with submerged vegetation strip

2017· article· en· W2770296624 on OpenAlexaff
Hossein Afzalimehr, Mahboobeh Barahimi, Jueyi Sui

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Water Management · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCobbleFlumeGeologyHydrology (agriculture)Vegetation (pathology)Flow (mathematics)Soil scienceGeotechnical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

To better understand the influence of non-uniform flow in coarse-bed streams with a submerged vegetation strip on flow characteristics, data were collected from experiments in a laboratory flume and field investigations in a cobble-bed river reach in central Iran. The results of the experiments and field observations showed that the velocity profiles for non-uniform flows in cobble-bed streams with vegetation strips can be divided into either two or three sub-zones. The velocity profiles of non-uniform flows in the laboratory flume and cobble-bed stream deviated from the log law in the wake and mixing zones, but fitted approximately in the log-law layer zone. The distribution of Reynolds stress for non-uniform flows was influenced by the vegetation strip and showed a non-concave shape. Near the channel bed, sweep motion occurred more frequently than the ejection process. Close to the top of vegetation strip, the correlation coefficient (r u′w′ ) was negative, indicating downward momentum transport due to sweep and ejection processes. However, r u′w′ was positive near the water surface, indicating upward momentum by the vegetation strip due to inward and outward interactions. The study shows that laboratory results cannot be easily applied to natural rivers without considerable assumptions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.346

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 communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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 designBench or experimental
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

Citations17
Published2017
Admission routes1
Has abstractyes

Explore more

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