Strategies for reducing sampling errors in exploration and resource definition drilling programmes for gold deposits
Bibliographic record
Abstract
Sampling error is the degree to which the concentration of an element differs from the true element concentration of the material from which the sample was collected. Gold mineralization commonly exhibits sampling errors as large as 50–100%. As a result, collection and preparation of drill samples from Au mineralization can provide significant challenges for the geoscientist, largely because of the coarse particulate nature of Au. To avoid this, geoscientists have opted to collect and prepare larger drill samples to reduce the magnitude of this ‘nugget effect’. Unfortunately, the ‘nugget effect’ can cause sampling error at any stage of sample treatment when sub-sampling takes place. Knowledge of the magnitude of error at each sub-sampling step is necessary to identify strategies to reduce overall error. This is because reduction of only the largest component of measurement error will reduce total measurement error in the most numerically efficient and effective manner. With knowledge of the various components of sampling, preparation and analysis costs, the sample treatment strategy that will most cost-effectively reduce sampling error can be identified. Regrettably, the typical absolute and relative errors that occur during sample collection and preparation of sub-samples with finer particle sizes are not generally known for many Au deposits. Results from three Au drilling projects document the magnitude of sampling, preparation and analytical errors experienced, and range from 22 to 46%, 7 to 20%, and 1 to 13%, respectively. These results are derived from large, laboratory-blind, duplicate quality control/quality assessment (QA/QC) programmes involving sample treatment protocols that would be considered to be appropriate for coarse Au-bearing samples. These QA/QC programmes measured the sampling errors, and ensured that they were minimized on these projects. In general, results indicate that a very large component of total measurement error is introduced during the collection of the initial sample, and that subordinate amounts of error are introduced during preparation and analysis. As a result, undertaking extraordinary efforts to reduce preparation or analytical errors does not result in a significant total measurement error reduction. In contrast, the collection of larger initial samples can result in the substantial reduction of total measurement error.
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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 | 0.081 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".