3D conductivity models of Lalor Lake VMS deposit from ground loop and airborne EM data sets
Bibliographic record
Abstract
Lalor Lake is a VMS deposit in central Manitoba, Canada. The deep ore body is buried under the cover rocks up to 1000 m. Multiple EM data sets were collected to delineate the compact and conductive alteration zones and two data sets are available to us. The first is HELITEM, an airborne time-domain EM survey that covers the entire exploration area. The second is a ground loop EM data measured by SQUID magnetometers that have high precision at late times. The two data sets map the conductivity structures at Lalor Lake in different ways: the airborne survey covers a broad area but has limited resolving power at depth; the ground survey provides information about the deep targets through very late times but the measurements were made in a smaller area. Individual 3D inversions were carried out for both data sets assuming little a prior information. Both are able to recover the trace of the expected ore body, but the airborne model is smooth and the ground model contains highly conductive anomalies. Then we invert the ground data again with the airborne model as the reference model. The new inversion again confirms the existence of the VMS ore body but also rearranges the conductive material according to the constraints from the reference model. The new model differs significantly from the blind inversion model at the deposit scale. Based on the information from the inversion so far, we conclude both surveys have picked up signals from the ore body in different levels of detail. More analysis and further data are still required to better delineate the target’s geometry.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".