Bibliographic record
Abstract
The reduction of the water content of a tailings slurry may be referred to as ‘dewatering’ or ‘water removal’ as is done when water is separated by mechanical means such as thickening or the production of paste tailings, or as ‘water loss’, when natural processes of drying, seepage and entrainment remove water. Often the context is such that water removed by dewatering or water removal is available for re-use and water lost is not. Optimisation of water losses from tailings may involve either a minimisation of water losses, as may be desirable in areas where water is scarce, or maximisation of losses, such as may be an advantage when a dryer tailings product is more stable, allowing stacking and reducing the long term need for containment in a dam. A review is made of methods for water removal and methods for both maximising and minimising water losses. Illustrations are provided of tailings management systems in which both water removal and management of water losses are practiced in order to meet differing objectives of water recovery and development of stable tailings deposits. The factors that influence the efficiency and effectiveness of both water removal and losses are discussed, including clay content of tailings, placement management methods and the influence of climate from hot deserts to frigid tundra (evaporation, desiccation and ice entrainment). Some examples are provided of typical water losses achieved for different tailings types, different dewatering methods and different tailings distribution and management methods. A review is made of some of the issues that may arise with different tailings management systems ranging from mechanically dewatered (dry) and placed tailings, through partially dewatered (paste) tailings to evaporation dried stacked tailings to slurry deposits. Approval to publish this paper was not received prior to these proceedings going to print. This paper will be made available on the Internet on the ACG website, www.acg.uwa.edu.au, and at the following URL: http://www.infomine.com/publications/docs/TailingsOptimisation2009.pdf Paste 2009, Viña del Mar, Chile 139
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".