Full‐tensor magnetic gradiometry: Comparison with scalar total magnetic intensity, processing and visualization guidelines
Bibliographic record
Abstract
Abstract Full‐tensor magnetic gradiometry data have been collected commercially for the last few years. However, to date, there is still no clarity on how to compare these data to scalar total field surveys. Some users display the vertical gradient of the vertical component ( B zz ) and compare that to a first vertical derivative of total field with the caveat that ‘they are similar’. Others compute the length of the measured vector and call that total field. We establish the basic formulas to calculate total field from the tensor components and demonstrate this with a real data example from Thompson, Manitoba, Canada. Another key question is whether full‐tensor interpolation is required to obtain total field from tensor data. We compare the results from using a commercial full‐tensor interpolation algorithm with standard minimum curvature of the tensor components individually and with another open‐source code that uses a radial basis function interpolator on the individual tensor components. All three applications produced a total field grid of superior quality to that calculated from a scalar total field survey available for the area of study.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".