Detecting fine-grained gold in pedogeochemical samples from the Fifteen Mile Stream gold property, Nova Scotia
Bibliographic record
Abstract
Pedogeochemical exploration for gold is impeded by the 'nugget effect', a sampling error that causes extremely variable Au grades in geochemical analysis due to the collection of rare but large gold nuggets, adding noise to geochemical data that obscures dispersal patterns and makes exploration for gold extremely challenging. The conventional method used in avoiding the nugget effect involves taking large samples, because such samples likely contain numerous coarse gold nuggets, and thus exhibit reproducible grades and avoid the 'nugget effect'. An alternative method, developed in China for regional gold exploration in stream sediment samples involves a different rationale, and involves analyzing small samples. This method ostensibly circumvents the nugget effect, by lowering the probability of collecting the rare, large gold nuggets. In this research the small sample method was applied to B-horizon samples developed over a previously identified Au geochemical anomaly to determine if this approach can effectively be used in pedogeochemical exploration. Gold grades in 14 size fractions (from 0 to 250 μm) and sixteen 0.3 g replicate analyses from up to 30 soil samples have been compared. Results confirm that the small samples do contain variance-inducing coarse gold nuggets. This indicates that the small sample strategy does not produce an advantage over conventional (large sample) analysis in gold exploration. Although unsuccessful, this study provides significant insights regarding alternative approaches that could be successfully used in gold exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".