Jet Grouting for Seepage Control at Lac Des Iles (LDI) Water Management Facility
Bibliographic record
Abstract
Three low-permeability cut-off walls were required for seepage control along the upstream toe of two new dams as part of the Lac des IIes (LDI) water management facility works. The LDI palladium mine is located 135 km North of Thunder Bay, ON, Canada. Jet grouting was used to create the low permeability cut-off walls by installing overlapping soil-cement columns. Jet grout columns were constructed using the double-fluid process. A comprehensive quality control program was implemented to ensure that specified geometric properties and performance requirements of the cut-off wall were achieved. An acoustic column inspector (ACI) tool was used to verify the diameters of the pre-production test columns. All jet grout locations were pre-drilled to prevent difficulties resulting from geological conditions and to ensure adequate penetration into bedrock. A three-dimensional profile of each jet grout column installed was generated and updated daily based on the results of pre-drill hole alignment measurements completed at every hole. In-situ permeability testing was also conducted during the pre-production test program and during the installation of the production columns. Permeability testing was performed in-situ and on cast cylinders of soil-cement samples. The low permeability cut-off walls were successfully installed and satisfied the specified performance criteria. Jet grouting was performed between the months of February and May under challenging winter conditions. This paper focusses on the quality management program (QMP) that was implemented to successfully complete this project.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".