Effects of low-pass filtering on the calculated structural index from magnetic data
Bibliographic record
Abstract
Abstract Euler and extended Euler deconvolution applications use an assumed structural index (SI) or calculate the SI, respectively, for magnetic anomaly data within a specified window. The structural index depends on the source type: specifically, the rate at which the field produced by the source decays. We have examined the effects that the application of low-pass filtering to magnetic data has on estimating the SI. Using a simple low-pass filter, we derived the SI for filtered-field solutions directly over, and away from, a target based on the magnetic potential of a vertical dipole (SI = 2). We validated this approach by applying extended Euler deconvolution to synthetic and field examples. In general, filtered magnetic data will decrease the numerically determined SI to a value lower than the theoretical one. The slope and cutoff wavelength of the filter directly affect the estimated SI solutions. The results prove that one must take into account filtering for the application of Euler deconvolution to locate dipole anomalies for unexploded ordnance detection.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".