Magnetic Induced Polarization - using new technology for greater detection capability of deep and elusive mineralization
Bibliographic record
Abstract
The Magnetic Induced Polarization (MIP) method uses the measurement of magnetic fields to directly detect internal and external current flow from IP-generating targets, rather than the resultant surface currents as with conventional Electric Induced Polarization (EIP). Magnetometric Resistivity (MMR) measures the magnetic field produced by galvanic current flow to detect horizontal variations in resistivity. We focus primarily on the MIP method but since MIP and MMR data are collected simultaneously, we will treat them together where appropriate. MIP/MMR is insensitive to horizontal layering, and is especially suitable for regions with highly conductive cover where EIP and resistivity responses are sharply attenuated. Magnetic fields easily propagate through such conditions; therefore MIP/MMR is minimally impacted by conductive cover. The other major advantage of MIP/MMR, over traditional electrical IP and resistivity, is that it completely eliminates the need for measurement electrodes. Hence, it is effective in difficult ground contact conditions such as dry sandy soils, frozen ground, and rocky scree slopes. For inversion purposes, MIP has an additional benefit that magnetic fields can be measured in all three axes simultaneously, which provides significantly more information about target position and attitude. By using SQUID technology and remote referencing, we are able to improve the data quality and extract useful three component MIP and MMR data. We present a number of field trials using both frequency and time domain methods to analyze the MIP and MMR responses from porphyry copper, and unconformity uranium ore bodies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".