A study of gold ore for processability by gravity separation techniques
Bibliographic record
Abstract
Today’s concentrators deal with a lot of gold deposits comprising smaller ore bodies, having low concentrations of the metal and situated in remote areas. The cost of detailed exploration and a full-fledged processability study considering the time and labour required may appear to exceed the cost of metal recovered from a deposit or a particular ore body. This paper describes some approaches to examining the gold ores mined at such deposits for processability and to developing gravity separation processes, which help save the time and cost of research without affecting the quality of resultant data. This research relied on the GRG test developed by Knelson in Canada, as well as a stage test developed by Institute TOMS in Russia (designed to determine optimum grinding size and number of processing stages). A simulation study was conducted to understand the recovery of gold during the grinding cycle (Stage 1) and to examine the Stage 2 process in a KC-CVD concentrator including concentrate refinement. The authors determined the distribution size of the feed material for each GRG test stage, documented the total percentage of gold recoverable by gravity separation as a function of the ore size, and established how the ore size and the KC-CVD concentrate output influence the gold recovery. This research study resulted in a process flow chart indicating the concentration performance based on gravity separation techniques.
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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) | 0.000 | 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".