Reconciling airborne and ground geophysical outcomes in the Athabasca Basin, Saskatchewan, Canada
Bibliographic record
Abstract
SummaryThe Athabasca Basin, Saskatchewan Canada is the world’s premier location of high grade uranium deposits. Most occurrences show a close spatial association with graphite shear-fault zones in the basement rocks (overlain by resistive sandstone) and EM techniques have been used for over 30 years to help map these conductive features. While exploration initially focused on shallower parts of the Basin, current exploration is requiring investigation through thicknesses of sandstone well in excess of 500 m depth. With drilling costs typically now approaching CAN$0.5 million per hole in the deeper parts of the Basin, considerable efforts are being expended to define basement targets with as much spatial resolution as possible. Consequently, most companies are employing some form of ground geophysical surveys to try and sharpen the target focus prior to drilling. We have had the opportunity to compare airborne and ground surveys in a number of locations and have found that there can be considerable disagreement between the conductivity models derived from airborne surveys and those produced from ground EM (active and natural field) or DC resistivity surveys. A number of these examples are presented and discussed so as to better understand what are the likely sources of error and how best to manage the risk of multiple but non-conforming outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".