Case study — tailings dam construction in an arctic climate
Bibliographic record
Abstract
Agnico-Eagle Mines Limited, operates the Meadowbank Gold Mine, in Nunavut, within the arctic region of Canada where permafrost is widespread. Construction of their mine facilities began in 2008 and production commenced in the first quarter of 2010. The mine will consist of a series of open pits, with conventional processing and slurried tailings deposition within the tailings storage facility. The tailings facility is being constructed in stages through a series of perimeter dams and staged raises to provide adequate tailings storage capacity. The 2009 construction season included the construction of Stage 1 of one such structure, ‘Saddle Dam 1’, a 10 m high and 250 m long, lined rockfill structure, with Stage 2 to be constructed in 2010 to a height of 20 m and an overall length of 400 m. The geotechnical investigation determined that soil thicknesses were up to 12 m with permafrost below the active layer (approximately 1–2 m) and the upper 5 m being ice-rich material. The dam was founded on bedrock on the abutments, and on-ice poor soils within the main body of the dam. Ice-rich soils were blasted and excavated from beneath the upstream portion of the dam foundation. Blanket filters were placed above this area followed by the upstream filters and the installation of a linear low density polyethylene (LLDPE) liner on the dam face. The LLDPE liner was installed at temperatures between -15 and -25°C. This paper presents the case study for the successful construction of the structure in challenging arctic conditions.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".