Interlocking Elements to Control Erosion in Natural and Urban Ecosystems
Bibliographic record
Abstract
Advancements in comprehending soil erosion alleviation, relevant to both natural terrains and urban settings, have experienced notable growth in knowledge and products. Nevertheless, the increasing influence of climate change-driven forces, extreme weather events, and human-caused actions have resulted in reduced attainment of the desired results in erosion mitigation efforts. This paper aims to investigate how interlocking elements contribute to the reduction of soil erosion in natural landscapes and urban green spaces. This will be achieved by analyzing published materials, patents, installation instructions, manuals, and reports from organizations. Furthermore, we delve into novel interlocking products and emerging strategies like soft solutions and ecologically engineered blocks designed to effectively address soil erosion within vegetated habitats while enhancing the system’s capacity to adapt and withstand shifts in climate, curbing soil loss, and diminishing the speed of water runoff, consequently mitigating the potential for erosion. Geotechnical engineering and other erosion control solutions like biobased interlocking components and interlocking permeable blocks offer promise in safeguarding natural landscapes and urban infrastructure from erosion-related impacts. The geotextiles market, for instance, which was valued at over $7 billion in 2022, is anticipated to experience an annual growth rate of 6.6% from 2023 to 2030. This growth can be attributed to increasing environmental concerns related to soil erosion and the rapid urbanization occurring in developing countries. However, continuous progress in the economic viability and sustainability of these techniques and products is crucial to effectively achieve erosion mitigation goals in the face of a shifting climate.
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 | 0.001 | 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".