Joint processing of total-field and gradient magnetic data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The processing of aeromagnetic data to account for levelling has been improved using gradient data. Utilising multiple magnetometers allows measurements of magnetic gradients and minimises the diurnal variation and other common-mode noise. We develop an equivalent source technique for jointly processing total-field and gradient data that makes use of a well known but rarely used relationship between the derivatives of the magnetic field and the derivative of its source to relate both datasets to a common equivalent source distribution. This approach treats the observed gradients as an additional and independent dataset instead of being just supplemental information. The direct result of joint processing is a set of enhanced data that incorporates information from both types of observed data as well as a higher signal-to-noise ratio. The methodology of the joint equivalent source processing technique is presented and demonstrated with a field example. Our method diminishes higher frequency noise, accentuates mid-frequency signals, and has higher resolution than that of total-field data alone.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it