Bibliographic record
Abstract
Abstract Environmentally acceptable reclamation solutions for the decommissioned tailings ponds can be achieved by integrating the tailings deposits into the landscape either by turning the tailings pond/deposit into a lake and maintaining a permanent water cover over the deposit or by stabilisation of the deposits as a “dry” landform. Conceptually, the dry landscape reclamation involves three remedial steps: 1) Placement of an interim cover on the tailings surface to provide the consolidation load and create a stable working platform. 2) Building of a surface contour providing suitable run off conditions for the surface water. 3) Capping the surface with a final cover to control infiltration into the tailings. For reliable predictions of consolidation, proper sequencing of remedial steps/measures and cost efficient reclamation of soft tailings it is necessary to use Non Linear Finite Strain (NLFS) codes. The advantage of the NLFS program system Consol2D is that it allows the reconstruction of the history of the tailings discharge, the evaluation of the material parameters, the calculation of settlement in inhomogeneous deposits, the 3D quantification of the settlement trough and the evaluation of the drainage effect of the vertical drains.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".