Examination of the Effects of Solids Content on Thickened Gold Mine Tailings Sedimentation and Self-Weight Consolidation
Bibliographic record
Abstract
Abstract Thickening is being increasingly adopted by the mining industry because of its economic and environmental attributes, such as decreased amounts of water released following deposition and a smaller footprint of the tailings site. This study presents an assessment of the continuous process of sedimentation and the self-weight consolidation of slurry and thickened mine tailings. The results of a series of settling column tests performed with specimens with solids contents between 50 % and 72 % are presented and discussed. Lower solids contents that are more characteristic of slurry tailings were also included to cover a wide range of settling behavior. High-precision monitoring of pore water pressure was used to identify the transition from sedimentation to self-weight consolidation, which likely occurs between solids contents of 65 % and 68 % for this material, and it highlighted the fact that the combination of these two settlement processes accelerates ue dissipation. The displacement results for the tailings–water interface corroborate values in the technical literature related to the settlement of suspensions. Equilibrium was reached within a narrow time range (i.e., 400 to 500 min) despite the wide range of initial solids content in the slurries tested (i.e., 50 % S to 65 % S).
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".