Experimental Investigation of Cement Mixing to Improve Lake Agassiz Clay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep mixing method (DMM) is a ground stabilization technique using lime or cement binders. The DMM has gained increasing applications to minimize ground settlements and increase stability to support structures. However, this ground improvement technique has not yet been used extensively in Manitoba. For the effective design of DMM, mixtures of cement, clay, and water that would produce optimal strength and stiffness are generally determined through laboratory tests. A total of 120 unconfined compression tests were conducted at different amounts of cement in the admixtures, water-cement ratio, and curing periods were conducted on Lake Agassiz clay frequently found in Manitoba. The results were compared with those of Champlain clay found in Ontario and Quebéc and Ariake clay in Japan where extensive data are available. This paper discusses the outcome of the comparison. It was found that the improvement effect of Lake Agassiz clay by DMM is an effective means to increase the strength.
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.
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 | 0.000 | 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) | 0.000 | 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 it