2D inversion of 3D magnetotelluric data: The Kayabe dataset
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
In the last two Magnetotelluric Data Interpretation Workshops (MT-DIW) the participants were asked to model the Kayabe magnetotelluric dataset, a dense (100 m) grid of thirteen lines, with thirteen stations in each line. Bahr’s phase-sensitive skew and the Groom and Bailey decomposition were used to select those lines for which the data could be considered two-dimensional. For these lines we used a 2D inversion algorithm to obtain a series of resistivity models for the earth. Finally, we constructed a 3D model using the 2D models and critically examined the validity and practicality of this approach based on 3D model study. We found that in the Kayabe dataset case the common practice of using 2D models to depict 3D models, can only be used to create a starting model for 3D interpretation. The sequential 2D models as a representation of a 3D body is unacceptable in terms of fit to the observed data. We question the validity of some of the conductivity structures in the 2D models, as they can be mere artifacts created by the algorithm to match 3D effects.
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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 | 0.000 |
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