MétaCan
Menu
Back to cohort
Record W1973302051 · doi:10.1115/1.4029608

Influence of Vegetation on Turbulence Characteristics and Reynolds Shear Stress in Partly Vegetated Channel

2015· article· en· W1973302051 on OpenAlex

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

VenueJournal of Fluids Engineering · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsTurbulenceVegetation (pathology)Reynolds stressShear stressEnvironmental scienceHydrology (agriculture)Reynolds numberTurbulence kinetic energyOpen-channel flowGeologyGeotechnical engineeringGeographyMechanicsMeteorology

Abstract

fetched live from OpenAlex

From the perspective of vegetation density, this research studies the influence of vegetation on turbulence characteristics and Reynolds shear stress in partly vegetated channel via a series of experiments. Natural reed is employed to simulate the emergent vegetation in rivers. Different vegetation densities including vegetated and unvegetated cases are considered in the research. The results of the research demonstrate that emergent vegetation may force the water flowing from vegetated areas to unvegetated areas and the forcing intensity increases with reed density. It is also found that the relative turbulence intensity declines along the vegetated channel in the direction of flow. Vegetation is found to reduce the total-average Reynolds shear stress therefore reduce the soil erosion. However, the Reynolds shear stress reduction is found disproportional to the vegetation density, and an optimal vegetation density range is quantitatively determined in the research. The findings of the research are significant to the practice of river ecological restoration.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.285

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)0.0000.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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