Duplicate sampling and the retention of archival diamond drill-core: no longer a contradiction
Bibliographic record
Abstract
Geoscientists undertaking mineral exploration sometimes are provided with historic geological and geochemical information about a prospective mineral deposit that they would like to use in a modern mineral resource assessment. Unfortunately, these historic datasets may lack critical information describing the quality of the data, such as duplicate samples, that are required under today’s disclosure regulations. As a result, geoscientists sometimes need to generate such data quality information after the fact ( a posteriori ). This contribution describes how duplicate samples can be obtained from historic drill-core to provide an unbiased assessment of grade and measurement error, as well as ensuring equivalent geostatistical ‘support’ in all samples, while at the same time retaining at least one quarter of the drill-core in archive for all sample intervals. It also describes an alternative method that can be applied to unsampled drill-core ( a priori ) to acquire unbiased grade and measurement error estimates, ensure equal sample support, while at the same time retaining half of the drill-core in archive.
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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 | 0.127 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".