Potential field continuation between arbitrary surfaces — Comparing methods
Bibliographic record
Abstract
ABSTRACT The continuation of potential field data from one irregular surface to another, not always horizontal, is often a necessary component within the data processing and interpretation stream. The most common requirement is to reduce field values (or some related component or derivative) to a horizontal plane, to facilitate further quantitative processing. Methods available to continue data comprise two main approaches. The first (source-based) involves calculating a source distribution that produces a fit to the data and can be used to calculate the field at any other point above. The second (field-based) requires no source determinations and deals with only fields but may involve calculating the field on some intermediate surface. Nine different continuation methods were compared (four source based and five field based) through synthetic tests and on real data from a helicopter-borne survey in Yukon, Canada. The preferred methods of Guspi and Hansen are those that do not involve any theoretical or geometric approximations and involve intermediate calculations on a plane or surface close to the observation surface. The Guspi approach is faster, based on using frequency-domain processing, but the Hansen method uses equivalent sources close enough to and consistently below the observation surface so that no low-pass filtering needs to be used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".