Bibliographic record
Abstract
Abstract Following a detailed investigation of the Fourier-domain differential reduction-to-the-pole (DRTP) algorithm I compared the results to those obtained using a space-domain reduction-to-the-pole algorithm. I demonstrate that DRTP reduces magnetic anomalies to the pole more effectively than the space-domain algorithm. The DRTP operator has singularities at the geomagnetic equator and enhances north-south trending features at low latitudes. The operator is modified by slightly increasing the inclination of the core field at low latitudes to suppress the singularity. This space-domain modification only affects the anomalies very close to the equator. The modified DRTP operator successfully reduces the magnetic anomalies at low latitudes to the pole. The effects of random noise added to the original magnetic anomalies are investigated in some detail, and an appropriate directional low-pass filter is used to remove the resulting enhanced noise in the reduced-to-the-pole magnetic anomalies. Very simple bodies (uniformly magnetized, cubic, or rectangular) are considered to clearly illustrate the effects of the DRTP, its modified version, and the directional low-pass filter.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".