Deep exploration technologies for illuminating highly prospective ground in the shadow of headframes
Bibliographic record
Abstract
In the last few years, many companies have purchased abandoned mines. This gives ready access to economic mineralization that was either ?missed? with previous generations of geoscience technologies and methods, or that represents ground not yet evaluated. Today, new deep geophysical technologies are helping with investigations ?in the shadow of headframes? - assisting not only in exploration, but also in ore delineation and mine development (ground condemnation). However, brownfield work is not easy. Cultural noise, scheduling, electrical noise, remoteness and resistance to new technologies are some of the traditional obstacles that have been overcome through deep electrical imaging and Distributed Acquisition Systems. DAS technologies have a large multi-channel, fixed receiver array; sensitive electronics; advanced processing and noise removal; and other characteristics that result in improved depth of penetration, data quality and detectability. Numerous brownfield sites have been surveyed over the past 5 years. In this paper, we review the components and capabilities of DAS systems, and specifically, Titan 24 Deep Earth Imaging for brownfield work, including near mine and minesite applications. Three case studies are presented, including two from porphyry copper environments in western Canada as well as a gold project from Bulgaria. These case studies represent the state-of-the art in geophysics for brownfield work and are a unique and novel application for today?s DAS technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".