Bibliographic record
Abstract
Numerous ground improvement technologies have been developed over the last few decades to address problematic soils, marginal sites, and geohazards. Soil erosion is a leading geohazard causing infrastructure damage during storm and flooding events. Researchers have studied various bio-treatment methods to decrease erosion susceptibility of coarse-grained soils. Bacterial Enzyme Induced Calcite Precipitation (BEICP) was explored in this study to increase undrained shear strength and decrease soil erosion from moving water. This research investigates the surface erosion control for the mixture of 20-30 standard Ottawa sand and Iowa Western loess silt stabilized by BEICP applied by a spray method. The results obtained in this study indicate that the higher enzyme concentrations increase the surface shear strength and that the formation of the calcite precipitation provides increased resistance to erosion. The depth of the calcite precipitation into the soil specimen was also investigated.\nSoft soils are also considered problematic soil due to their low undrained shear strength and compressibility. Various methods have been used to increase the shear strength such as addition of fibers, shredded rubber tires and geosynthetics. This research investigates adding magnetic particles and using a magnetic field to rotate the particle orientation to increase the shear strength of soft soils. A soft soil surrogate (laponite) which is also a transparent material, was used to visualize the rotation of the magnetic particles. The addition of the magnetic particles was shown to significantly increase the undrained shear strength. Preliminary work using a controllable electro-magnet to create a magnetic field to rotate the orientation of the magnetic particles at small scale is also presented.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".