A multimodel method for depth estimation from magnetic data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The local wavenumber and a multimodel wavenumber are complex attributes derived from a complex analytic signal. These quantities have been used to interpret anomalies arising from contacts, thin sheets, and horizontal cylinders. A new multimodel wavenumber can be used for computing depths of 2-D thick dikes and 2-D sloping steps. These two multimodel wavenumbers have been incorporated into a depth-estimation algorithm based on automatic curve matching. This algorithm works on profile data and has three appealing features: (1) the most appropriate of these five models is selected automatically; (2) the automatic curve matching uses a least-squares technique to reject responses that do not conform to the model assumptions; and (3) interference from distant sources can be accounted for as a base-level shift of the multimodel wavenumber curves. Applying the automatic technique to survey data from the Western Canada sedimentary basin yields four thick dikes between 3400 and 4300 m below sensor. These depths are equivalent to 2.2 and 3.1 km below sea level, which is consistent with the basement depths derived from drillhole information. Using these solutions as a starting point in an iterative forward modeling exercise, the measured data were explained with a geologically reasonable model.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it