Sensitivity cross-sections in airborne electromagnetic methods using discrete conductors
Bibliographic record
Abstract
A versatile discrete conductor model is used to generate the maximum signal-to-noise ratio along an airborne electromagnetic (AEM) profile. By varying the position of the conductor below and to the side of the airborne traverse, a sensitivity cross-section can be generated that shows the volume of material that influences the AEM response. This type of section accounts for both the coupling of the transmitter with the model and the coupling of the induced current flow with the receiver. Some previous definitions of ‘volumes of influence’ sometimes called ‘footprints’ do not take into account the coupling of the primary field to the target and the secondary field to the receiver. The versatile discrete conductor model can also account for target strike (variable orientation of the current flow) by considering only specific components or orientations of the primary field at the conductor. For a vertical dipole transmitter, the vertical or z-component receiver is generally better for detecting targets at greater depth and the lateral detection range is maximum for the transverse or y component. The in-line or x component is best for sensing conductors where the currents are constrained to flow in a vertical plane perpendicular to the flight direction of the AEM system. The sensitivity cross-sections can also be used for survey design: for example, in order to ensure effective exploration down to 200 m the HeliGEOTEM system must fly with a flight line spacing of 210 m, whereas the more powerful MEGATEM system can achieve equivalent depth penetration with a 300 m line spacing. The discrete conductor model could also be used to estimate the ‘volume of influence’ in ‘moving footprint’ 3D inversion schemes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".