Targeting epithermal Au-Ag using helicopter TDEM, magnetic, and radiometric data at Lawyers Project, North-Central BC, Canada.
Bibliographic record
Abstract
In September 2018, Geotech Ltd. completed a VTEM helicopter time-domain electromagnetic, magnetic and radiometric survey on behalf of Benchmark Metals Inc. over the Lawyers property, in northcentral BC. The magnetic results reveal a strong spatial relation ship between sharp magnetic lineaments and the known mineralization. Radiometric results show that mineralization is characterized by hydrothermal alteration resulting in potassium enrichment, manifested as K/Th highs. The VTEM electromagnetic results identified local EM anomalies representing both discrete and structural conductors. However, none of the EM anomalies making up conductive zones coincide with the known epithermal mineralization, instead all the known Au-Ag deposits and occurrences are located in zones of high apparent resistivity. Subsequent analysis of the VTEM data analysed using AIIP mapping revealed that all the known Au -Ag mineralized zones coincide with moderate to high Cole-Cole time constant (TAU) anomalies, consistent with relatively coarse-grained polarizable material, such as disseminated sulphides or hydrothermally altered clays. The previous targeting approach focused on individual analyses of magnetic, structural, radiometric, EM resistivity and AIIP results, then arriving at a targeting model, based on geologically and geophysically based considerations. A new approach for targeting uses a semi- automated, machine-learning (ML) assisted approach that includes: Structural Complexities (SC), Self-Organizing Map (SOM) classifications, and Supervised Deep Neural Network (SDNN) targeting of the geophysical data. The new targeting approach has further reduced the number of priority targets from previous five (5) to three (3), which includes most of the known epithermal Au -Ag occurrences, as well as two areas for follow-up.
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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 | 0.000 |
| 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 | 0.000 |
| 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".