An assessment of topographic effects on airborne and ground magnetic data
Bibliographic record
Abstract
Recent advances in magnetic surveying have focused on achieving higher levels of instrument sensitivity and better definition of the morphology of the magnetic field through the use of measured magnetic field gradients. Images derived from these high-resolution magnetic surveys are widely used as a direct proxy for geologic mapping, especially in areas of limited surface exposure. Commonly, this involves the application of skeletonization (e.g., multiscale edges, or “worms”), Euler, and/or wavelet-based processing routines to generate estimates of the location, and morphology of the edges of anomalous source bodies. The primary assumption for all of these image- (map-) based data processing routines is that the observed magnetic data set provides an unbiased representation of the magnetic mineral variation in the surface and subsurface geology. This assumption may be valid when the observed magnetic anomalies are greater than 5000 nT and the topography is relatively flat, but it is certainly not valid when the observed anomalies are less than 100 nT and topographic variations exceed 100 m. Indeed, in some situations, topographic variations of less than 20 m can lead to geologically erroneous conclusions derived from ground magnetic surveys.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".