A discrete conductor transformation of airborne electromagnetic data
Bibliographic record
Abstract
ABSTRACT Airborne electromagnetic moment data are transformed to two depth sections that display the properties of a spherical conductor. In addition to the spherical conductor being small or distant, we made other simplifying assumptions: the conductor is always below the traverse line and the strike of the plane containing the current flow is always perpendicular to the flying line. The transformation algorithm compares measured moment data with synthetic moment data in a look‐up table. An index of fit is calculated to measure how closely the data in the look‐up table match the measured data. When the fit is good, we use bright colours on the depth section to indicate the properties of the discrete conductor; when the fit is poor, we make the section grey. The estimated properties displayed on the two sections are the product of conductivity and the square of the radius (CRS) on one section and the dip of the current flow on the other section. The position and depth can also be inferred from the location where the fit is best. The data displayed on the depth sections are also summarized in plan view, with colour being used to indicate the estimated properties of the sphere (CRS, dip and depth). This colour map can be displayed in combination with a greyscale image of the electromagnetic data as this illustrates the context of the colour features. Application of the method to two field data sets shows that the method works well for isolated bedrock conductors. However, no features were resolved when there was interference from nearby conductors. Also, we found that wide bodies were not necessarily well resolved. In some cases the features on the sections were confusing, but this can reflect data that is complex or difficult to interpret. Where coherent features are displayed, the estimated values of the depth, CRS and dip seemed reasonable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".