Good practice in the design and use of large sluice boxes
Bibliographic record
Abstract
Small-scale gold mining is an important source of livelihood for many poor people in the developing world. The gold recoveries by small-scale miners are notoriously poor but a scoping study, (Styles et al 1999) showed that there was considerable potential to improve the situation. The main way of recovering gold from alluvial gold ores is sluicing, but the small-scale miners often have a poor understanding of the principles of the operation of a sluice box or how to make it work better. This report contains information to give Mining Engineers, Mines Officers and Mining Technicians in developing countries a good understanding of the design and operating principles of sluice boxes. This will enable them to give good advice to small-scale miners about ways to make their mining operations more efficient and improve their gold recoveries. The report is aimed at alluvial miners in areas where water is readily available and relatively large sluice boxes are used. It is based on experience in Guyana and is particularly relevant to gold mining in south America and south-east Asia. The gold recovery process is described from the delivery of ore to the box through to extraction of gold from final concentrates. At all stages of the process the principles are explained and recommendations given on the best practice. This covers the design, construction and operation of the various components of a sluice box. Particular attention is paid to the gold trapping system in the box; the riffle system and the mats. This is an aspect that often receives little attention but has profound effects on the gold recovery and can be relatively easily improved. A system of mats and riffles developed in Canada is readily applicable to small-scale mining has been demonstrated and is described in the report. It is important to have the correct water flow conditions in the box for the gold trapping system to work efficiently. Ways to test the conditions are given and the design features that need to be modified to achieve optimum flow are described. In addition advice is given on ways of minimising the adverse environmental impact of alluvial gold mining, both on decreasing the use and release of mercury and spoiling of water resources. The importance of testing both the alluvial ores and the products of the mining operations is stressed. This helps to ensure that appropriate gold recovery methods are being used for the type of ore being mined. It also keeps a check on the efficiency of the gold recovery and gives immediate warning of problems.
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 | 0.076 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.041 |
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".