Experimental Study to Determine an EICP Application Method Feasible for Field Treatment for Soil Erosion Control
Bibliographic record
Abstract
The end goal of this research is assessing the feasibility of using enzyme induced carbonate precipitation (EICP) to create a cemented top layer to control runoff erosion in sloping sandy soil. The paper presents the results of an experimental study of bench-scale tests on EICP-treated sands to determine a treatment method feasible for field placement for this application. The soils tested were two natural sands and Ottawa 20–30 sand used as control. The EICP application methods were percolation by gravity, one-step mix-compact, and two-step mix-compact. Other conditions considered were pre-rinsing the sand prior to treatment, adjusting soil pH prior to treatment, and changing the EICP solution concentration. Promising results for this field application were obtained using the two-step mix-compact when the soil was first mixed with the urease enzyme solution before compaction. Considering that the EICP reaction starts once all components are added, this method would ensure that the reaction does not take place before the protective layer of treated soil has been installed. The effect of pre-rinsing the natural sand was not consistent throughout the testing conditions and its role in improving soil cementation in natural sand needs further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".