Enhancing uranium exploration through seismic methods and potential field modeling at the McArthur River mine site, Saskatchewan, Canada
Bibliographic record
Abstract
In this paper we introduce initial results from the McArthur River Integration Project (McRIP), an ongoing multidisciplinary study of the geology, geochemistry and geophysics on the McArthur River mine property. Currently, results are focused on the combined interpretation of 2D, 3D surface, and borehole seismic survey interpretations integrated with historical and new drilling results, statistical analysis of gamma well logs, and underground mine mapping. By modeling these datasets in 3D and combining them with the seismic interpretations (at various resolutions), we have gained a better understanding of the two dominant uranium ore controls: 1) the paleotopography of the sandstone‐basement unconformity; and 2) the varying amounts of unconformity offset resulting from moderately to steeply‐dipping, cross‐cutting fault systems. Tests of the interpreted geophysical model with two underground drill holes required modifications of the seismic model to reconcile it with the geology. Magnetic and gravity inversions aided in the reinterpretation of the unconformity surface in the vicinity of these drill holes. The McRIP project, though still in its infancy, is changing the way we interpret and visualize all types of data, while providing further evidence that seismic methods are a valuable, if underutilized, tool for both uranium exploration, and strategic mine planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".