The trapping efficiency of vegetation: wind tunnel investigation
Bibliographic record
Abstract
Vegetation distributed a cross a surface provides significant protection against wind erosion in three ways. First, by extracting momentum form the wind flow reducing the shearing stress acting at the surface. Second, the vegetation shelters the surface from the erosive force of wind by covering a portion of the surface and finally, vegetation traps the soil particles in transport thereby acting as a catchment for sediment deposition, which was the core of this investigation. This study was conducted to directly measure the rates of sand deposition and sand flux within a range of roughness concentration and element characteristics in order to determine the range surface cover needed to effectively reduce sand transport. A series of porous uniformly spaced arrays of non-erodible roughness elements were used in this investigation. The elements used in this investigation were constructed as clusters of dieldrin rods, vertically oriented forming porous non-erodible elements that were cylindrical in overall shape. In agreement with previous studies, sand deposition within the arrays was strongly related to the roughness concentration ([lambda]). Similarly, the rate of sand deposition was influenced by porosity such that a decrease in porosity led to reduced sand deposition. In this study, the Guelph Laser Profiling System was used to generate surface elevation contour maps. The generated maps demonstrated that the lee-side deposition with the elements varied with element concentration, roughness geometry and the wind speed, with most deposition occurring at [lambda] = 0.28 with u'f' of 10.4ms -1. A non-dimensional parameter R'eff' is introduced to quantify the effectiveness of the elements on the surface.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".