A multimodel method for depth estimation from magnetic data
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".