Results from a galvanic HeliSAM survey over the Patterson Lake South uranium deposit
Bibliographic record
Abstract
A galvanic HeliSAM survey was undertaken over the Patterson Lake South uranium deposit in 2016. The galvanic HeliSAM survey uses a large grounded electric dipole and a mobile cesium vapor magnetometer to simultaneously yield magnetic, magnetometric resistivity and electromagnetic data. Inversion of the MMC data was completed with two approaches, an approximate gravity inversion analogue and a full 3D solution of Maxwell‖s Equations. The gravity 3D inversion analogue was found to be poor substitute for full 3D EM inversion. The HeliSAM survey detected numerous structural features and outlined portions of the north and south mineralized conductors. It also confirmed cross structural features inferred from the DC Resistivity survey. It appears results from the galvanic magnetometric conductivity (MMC) mode of operation are crucially dependent on the placement of the ground current electrodes for complex trends, so care must be taken for grounded electrode placement. The similarity of inverted MMC/TFEM and the inferred structures very likely makes it a good replacement for EM and gradient resistivity. Ground DC resistivity surveying, with multiple current injection locations, will provide better results for more complicated features, but with a downside of greater cost. With proper use, the HeliSAM system is a powerful new tool in the geophysics collection. Presentation Date: Tuesday, October 16, 2018 Start Time: 9:20:00 AM Location: Poster Station 15 Presentation Type: Poster
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".