Synthetic model testing and distributed acquisition dc resistivity results over an unconformity uranium target from the Athabasca Basin, northern Saskatchewan
Bibliographic record
Abstract
The dc resistivity method has become a preferred reconnaissance mapping tool for uranium exploration targets in the Athabasca Basin, in northern Saskatchewan, Canada. Regionally, uranium deposits can occur beneath <100 m to >1 km thick sandstone cover rocks, are commonly associated with deeper basement graphitic metasedimentary units and are also often accompanied by clay-alteration zones in the sandstones. As a result, deep-penetrating electromagnetic and electrical geophysical techniques are ideally suited for indirect exploration of these types of deposits. A variety of electrode configurations are being used, however the pole-pole array is currently favoured in the Athabasca Basin due to its high signal levels, its deep penetration and its anomaly resolution. More recently, however, other technologies such as audio-magnetotellurics (AMT/MT) and 24-bit A/D distributed acquisition systems (DAS) have been introduced to extend the depth of exploration below 1 km. In the example presented here, a DAS acquisition system was used to acquire dc resistivity data, using a variety of electrode arrays, to examine the response parameters from the different configurations along a single line located along a known conductive trend at the M-Zone on the Wheeler River property.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".