An airborne electromagnetic system with a three-component transmitter and three-component receiver capable of detecting extremely conductive bodies
Bibliographic record
Abstract
ABSTRACT Extremely conductive bodies, such as those containing valuable nickel sulfides, have a secondary response that is dominated by an in-phase component, so this secondary response is very difficult to distinguish from the primary field emanating from the transmitter (because by definition they are identical in temporal shape and phase). Hence, an airborne electromagnetic (AEM) system able to identify the response from the extremely conductive bodies in the ground must be able to predict the primary field to identify and measure the secondary response of the extremely conductive body. This is normally done by having a rigid system and bucking out the predicted primary (which will not change significantly due to the rigidity). Unfortunately, these rigid systems must be small and are not capable of detecting extremely conductive bodies buried deeper than approximately 100 m. Another approach is to measure the transmitter current and geometry and subtract the primary mathematically, but these measurements must be extremely accurate and this is difficult or expensive, so it has not been done successfully for an AEM system. I exploit the geometric relationship of the primary fields from a three-component (3C) dipole transmitter. If the transmitter is mathematically rotated so that one axis points to the receiver, then linear combinations of the fields measured by a 3C receiver can be combined in such a way that the primary fields from the transmitter sum to zero and cancel. Alternatively, the measured transmitter current and response could be used to estimate the transmitter-receiver geometry and then to predict and remove the primary field. Any residual must be the secondary coming from a conductive body in the ground. Hence, extremely conductive bodies containing valuable minerals can be found. An AEM system with a 3C transmitter and a 3C receiver should not be too difficult to build.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".