Self-weight consolidation of mixtures of mine waste rock and tailings
Bibliographic record
Abstract
Mixtures of waste rock and tailings are compared with unmixed waste rock and tailings in a column study of self weight consolidation. Standard practice for surface mine waste disposal produces the two individual waste streams of waste rock and tailings. Waste rock dumps offer high strength and low compressibility characteristics but are prone to oxidation and metal leaching because of their high permeability and unsaturated conditions. Tailings deposits typically have low permeability and slow time rate consolidation properties but also have end land use issues and long term stability problems related to shear strength. Three mixtures of waste rock and tailings were loaded into columns and monitored for settlement, drainage, and pore-water pressure response for 100 days. A fourth column was built with waste rock only as a control. Mixtures with approximately 5:1 waste rock to tailings by dry mass were found to have a hydraulic conductivity similar to tailings alone and total settlements similar to waste rock alone. Mixture materials also remained saturated during the 100 day test. Results indicate that mixing waste rock and tailings for disposal is a promising idea that may help eliminate problems arising from current practices in mine waste disposal.Key words: co-disposal, hydraulic conductivity, self weight consolidation, tailings, waste rock.
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 | 0.000 | 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) | 0.001 | 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".