Detection of a perfect conductor with an airborne electromagnetic system: The Gemini Field Test
Bibliographic record
Abstract
ABSTRACT The cores of high-grade nickel and copper sulphides appear as “perfect conductors” to most electromagnetic (EM) and airborne electromagnetic (AEM) systems, because they have bulk electrical conductivities of the order of 100,000 S/m. The EM response of these highly conductive cores is essentially undetectable with off-time measurements or when using nonrigid towed-bird systems. Compact AEM systems with accurate primary field bucking and on-time or in-phase measurements are sensitive to perfect conductors, but are incapable of detecting deep targets. Using a GPS system to define geometry, calculations suggest that it should be easy for an AEM system to detect “perfect conductors” provided the receiver was several hundred meters distant from the transmitter. A twin (Gemini) aircraft test was undertaken to test this concept in 2005. The field test successfully demonstrated detection of very conductive targets. Errors associated with geometric changes were better than 0.5% of the primary field at 400 m separation, allowing detection and characterization of the 30 Hz, in-phase response of small and extended conductors. The test shows that a 200 × 100 m very-strongly conductive thin-sheet target would be detectable to depths of 200 m below surface using off-the-shelf technology. Larger conductors would be detectable at greater depths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".