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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".