Using spatial derivatives of electromagnetic data to map lateral conductance variations in thin-sheet models: Applications over mine tailings ponds
Bibliographic record
Abstract
ABSTRACT Mine waste, variable overburden, and the saprolite associated with nickel laterites have conductivity thicknesses (conductances) that vary laterally. In order for electromagnetic methods to be used to easily map lateral changes in conductance over thin-sheet-like bodies such as these, a simple conductance estimation method has been developed from Price’s equation. Through forward modeling, we found that assuming a uniform conductance and solving for an apparent conductance was sensitive enough to identify lateral conductance changes. The method was independent of the transmitter location, and each measurement provided a direct estimate of the apparent conductance below that station. The receiver can be moved around quickly allowing for lateral variations in apparent conductance to be determined efficiently. However, one of the required terms in the equation used is the vertical derivative of the secondary vertical magnetic field (dHzs/dz). The accurate measurement of spatial electromagnetic derivatives requires a good signal-to-noise ratio (S/N), which can be hampered by low derivative signal values. Field studies performed over a dry tailings pond in Sudbury, Ontario, Canada, showed that an S/N greater than three was achievable even with dHzs/dz values of less than 0.5 pT/(Am). Apparent conductance estimates revealed that the tailings had a large resistive zone associated with surface vegetation, which may be correlated with favorable growing conditions and/or less conductive or thinner tailing material. Larger apparent conductances in other areas may be related to zones of thicker tailings and/or more conductive material (possibly due to increased metal content). Further drilling and sampling work is required to answer these ambiguities. Regardless, mapping the conductance of a thin sheet is an important step toward assessing if there are leftover metals in mine waste. However, the developed method is general and can be used in many other situations involving laterally varying thin bodies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".