International Practices and Guidance: Natural-Fiber Rolled Erosion Control Products
Bibliographic record
Abstract
In recent years, there has been a great deal of interest in the development and use of natural-fiber rolled erosion control products (RECPs) to sustainably manage soil erosion. Natural fibers offer many advantages over synthetic fibers in that they are biodegradable, can absorb water, and can easily conform to underlying soil surfaces. In the US, coir, jute, straw, and wood excelsior fibers are commonly used to manufacture RECPs; however, efforts are being made around the world (e.g. United Kingdom, Canada, and the US) to explore potential uses of other natural fibers, such as hemp, flax, sugarcane, peanut shells, palm leaves, and cotton. Many researchers have characterized the properties of natural-fiber RECPs and documented their successful use in erosion control applications. For example, work has been done in India to evaluate the physical and engineering characteristics of coir and jute fibers for use in erosion control. Research efforts in the US and Europe have focused on the development of standardized test methods for characterizing RECPs and the performance of large-and small-scale tests. Many case histories have been published that document the successful use of natural-fiber RECPs. This paper presents an overview of natural-fiber RECP practices that are being used around the world and emergent fibers that are being evaluated for use as RECPs. International practices and guidance for the selection of natural-fiber RECPs for erosion control are given.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.022 |
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".