AEM mapping and imaging of the Izok lake Zn-Cu-Pb-Ag volcanogenic massive sulphide deposit in Nunavut Canada
Bibliographic record
Abstract
In August 2015, a VTEM helicopter time-domain EM survey was carried out over the Izok Lake Zn-Cu-Pb-Ag volcanogenic massive sulphide deposit in Nunavut, Canada. Originally discovered in the 1970’s, the objective was to test its response using modern AEM systems. Izok Lake is one of the largest undeveloped rich Zn-Cu deposits in North America, with a mineral resource of 15 Mt grading at 13% Zn and 2.3% Cu. The ratios of B-field and dB/dt Z time-constants (TAUs) of the AEM data are able to map surficial extent of the highly conductive deposit. Airborne Inductively Induced Polarization (AIIP) results map disseminated and fine grained sulphides and alteration products derived from the Izok deposit by glacial dispersal. Conductivity and resistivity depth imaging sections indicate that the high conductivity (or low resistivity) zones match very well with the deposit lenses. Furthermore, thin plate modeling of the deposits provides precise information on the depths and dips of the orebodies. The results of this study prove the effectiveness of applying modern AEM method in the exploration for VMS deposits in the Arctic Canadian Shield. Presentation Date: Wednesday, September 27, 2017 Start Time: 3:55 PM Location: 360C Presentation Type: ORAL
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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) | 0.001 | 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".