Bibliographic record
Abstract
Gravity-Recoverable-Gold (GRG) is defined as gold present in a particle in sufficient quantities as to be selectively recoverable from gangue via gravity methods. The McGill standard GRG test is an ore characterization test using three stages of sequential liberation and recovery with a Knelson KC-MD3 centrifuge to determine the size distribution of GRG. This thesis describes the development and testing of two simplified versions of the GRG test, using two and one stages of recovery respectively. Both tests use a feed mass of 20 kg, as opposed to the 40 to 100 kg normally used for the standard test. Eighteen differing ore samples were processed with the simplified GRG tests. For non-abrasive ores the one-stage simplified test returns a similar GRG content and size distribution, making the two-stage test superfluous. For abrasive ores, the one-stage test returns a GRG content that can be as much as 33% relative lower than that of the standard test, with a much finer size distribution. The two-stage test exhibited similar poor performance, though to a slightly lesser degree due to and additional stage of recovery attempted prior to grinding the abrasive material. The GRG lost typically reports to size fractions coarser than 25 µm, strongly suggesting smearing onto gangue particles. Because of the lower feed mass used, both simple tests are susceptible to the nugget effect; feed representativity also becomes challenging for ore samples of a head grade of 1 g/t or less.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".