Bibliographic record
Abstract
To control the risk of static or dynamic liquefaction, filtered tailings stacks are typically designed to be compacted and to remain unsaturated. In theory, compacting filtered tailings should be easy, since mines tend to produce a consistent, well-graded sandy silt or silty sand tailings. The filter presses are designed to produce tailings with a geotechnical moisture content within a narrow range; modest-sized equipment can compact the tailings in thin lifts; and traditional earthworks quality control methods are common and readily available. In practice, however, filtered tailings and mine owners often discover that the learning curve associated with compacting filtered tailings can be steeper than expected. The filter plant will typically produce tailings that are somewhat wet of the standard Proctor optimum moisture content, making compaction difficult. If not protected, tailings at the loadout can absorb water or freeze. The tailings stack must be kept graded to promote runoff. In some climates, evaporation may be insufficient for drying; in others, snow, ice, and freezing conditions present a challenge. At some mines, the tailings liquefy under cyclic loading by dozers, trucks, or compactors as they are being placed. Choosing a tailings field-density specification is not straightforward, especially where high stresses at the base of the stack can increase the risk of static or dynamic liquefaction. Method specs for compaction may be employed, but can be unreliable under certain conditions. A nuclear densometer often does not provide accurate readings of the density of some tailings, particularly those with elevated levels of metals. This paper presents practical, hard-won lessons and solutions from the field to aid in the design, operation, and closure of filtered tailings facilities based on firsthand experience in Canada and interviews with operators around the world, lessons that can help shorten the learning curve for new and existing filter stack operations.
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How this classification was reachedexpand
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 | 0.002 |
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 teacher head, 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".