Quantitative Magnetization Vector Inversion
Why this work is in the frame
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Bibliographic record
Abstract
Modelling of magnetic rock properties from magnetic field observations has been an important practice in resource exploration for decades. However, the application of this practice has been limited by conventional thinking that assumes rock magnetization is dominated by induced magnetization such that magnetization direction is aligned with the geomagnetic field. Convention has also accepted that we are unable to model for magnetic remanence without a-priori knowledge of remanence direction and strength.Recent practical successes in directly modelling magnetization vector direction and strength using Magnetization Vector Inversion (MVI) have challenged these conventions, and MVI modelling is proving useful in practical exploration scenarios. The addition of new information, namely the direction and amplitude of magnetization, demands new thinking and approaches to understanding what this information means, and how to use the modelled direction of magnetization in practical situations.This paper presents a new statistical and quantitative approach to define and discriminate different magnetization domains within a full 3D MVI voxel model. Our studies show that modelled vector direction is meaningful even without prior knowledge of remanence (and other) magnetization characteristics. We also demonstrate that reasonable magnetization direction can be recovered from both weakly and strongly magnetized source rocks.
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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) | 0.002 | 0.003 |
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